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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5eb2606b-2724-43be-83bb-fb4df67d0b35",
+   "metadata": {},
+   "source": [
+    "# Introduction to Data Science\n",
+    "<img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
+    "<br>\n",
+    "Contact author(s): Becky Nevin, Brian Nord<br>\n",
+    "Last verified to run: 2025-05-09 <br>\n",
+    "LSST Science Pipelines version: Weekly 2025_09 <br>\n",
+    "Container size: small <br>\n",
+    "Targeted learning level: intermediate <br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a4e60f3-a825-4dcc-8cd5-37ed43b7373b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext pycodestyle_magic\n",
+    "%flake8_on\n",
+    "import logging\n",
+    "logging.getLogger(\"flake8\").setLevel(logging.FATAL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05e4d2c6-9774-48db-930e-58536bd69de0",
+   "metadata": {},
+   "source": [
+    "**Description:** This notebook guides a PI through the process of using python's data science and machine learning libraries to explore data from complex ADQL queries with the TAP service. The goal is to build a predictive model to estimate missing $r-$band Kron Flux values when the other bands are available, and visualize the results and quantify the model performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "242eb338-4d88-4a17-8e35-20d80774ec1b",
+   "metadata": {},
+   "source": [
+    "**Skills:** Use of data science and machine learning tools such as scikit-learn, pandas, and seaborn."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76ffcb7f-a070-47b7-8545-f10813ff78ae",
+   "metadata": {},
+   "source": [
+    "**LSST Data Products:** Object, Forcedsource, and CcdVisit tables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf4d0080-92f9-4a67-a829-5a772e428ac4",
+   "metadata": {},
+   "source": [
+    "**Packages:** lsst.rsp, pandas, scikit-learn, seaborn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e354c692-b8b4-425f-bcbe-a35221a9993a",
+   "metadata": {},
+   "source": [
+    "**Credits:** Developed by Becky Nevin in collaboration with Melissa Graham, Brian Nord, and the Rubin Community Science Team for DP0.2. Based on notebooks developed by Leanne Guy (TAP query) and Alex Drlica-Wagner and Melissa Graham (Butler query).\n",
+    "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e3e438c-a028-417f-a21f-1d31c5946376",
+   "metadata": {},
+   "source": [
+    "**Get Support:**\n",
+    "Find DP0-related documentation and resources at <a href=\"https://dp0.lsst.io\">dp0.lsst.io</a>. Questions are welcome as new topics in the <a href=\"https://community.lsst.org/c/support/dp0\">Support - Data Preview 0 Category</a> of the Rubin Community Forum. Rubin staff will respond to all questions posted there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca5378d1-00fe-44ad-be88-5f1a0a85b404",
+   "metadata": {},
+   "source": [
+    "# 1. Introduction\n",
+    "\n",
+    "This notebook provides an intermediate-level demonstration of how to use the Table Access Protocol (TAP) server and ADQL (Astronomy Data Query Language) to query and retrieve data from the DP0.2 catalogs.\n",
+    "\n",
+    "TAP provides standardized access to catalog data for discovery, search, and retrieval.\n",
+    "Full <a href=\"http://www.ivoa.net/documents/TAP\">documentation for TAP</a> is provided by the International Virtual Observatory Alliance (IVOA).\n",
+    "ADQL is similar to SQL (Structured Query Langage).\n",
+    "The <a href=\"http://www.ivoa.net/documents/latest/ADQL.html\">documentation for ADQL</a> includes more information about syntax and keywords.\n",
+    "Note that not all ADQL functionality is supported yet in the DP0-era RSP.\n",
+    "\n",
+    "**See the recommendations for TAP queries in DP0.2 tutorial 02a \"Introduction to the TAP Service\".**\n",
+    "\n",
+    "The [documentation for Data Preview 0.2](https://dp0-2.lsst.io/) includes definitions\n",
+    "of the data products, descriptions of catalog contents, and ADQL recipes.\n",
+    "\n",
+    "## 1.1. Package imports\n",
+    "\n",
+    "Import general python packages, the Rubin TAP service utilities, and various scikit-learn utilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas\n",
+    "\n",
+    "from astropy import units as u\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "import sklearn.metrics as metrics\n",
+    "import inspect\n",
+    "from sklearn.utils import all_estimators\n",
+    "\n",
+    "from lsst.rsp import get_tap_service, retrieve_query"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90251edc-e77a-4f2c-aef3-935381faebc9",
+   "metadata": {},
+   "source": [
+    "Set up seaborn to use a friendly palette."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94acc9f6-2033-4ace-aefd-d036a35f4221",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set_style(\"whitegrid\")\n",
+    "palette = sns.color_palette(\"colorblind\")\n",
+    "sns.set_palette(palette)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca1e28f4-805e-4480-a7c6-0473b7e2b088",
+   "metadata": {},
+   "source": [
+    "## 1.2. Define functions and parameters\n",
+    "\n",
+    "Instantiate the TAP service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caf56589-100a-4481-8f24-5f5058b6671f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service = get_tap_service(\"tap\")\n",
+    "assert service is not None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eb0f20e-6c28-404f-8032-acc00f73405a",
+   "metadata": {},
+   "source": [
+    "Set the maximum number of rows to display from pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas.set_option(\"display.max_rows\", 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd325fe4-6c7c-4803-ad79-f30d7edc23e3",
+   "metadata": {},
+   "source": [
+    "# 2. Query for Kron fluxes around extended (galaxy) objects.\n",
+    "The Kron flux is a measurement of the total flux (or brightness) of an astronomical object, typically a galaxy or extended source, obtained using an elliptical aperture that scales with the object's light profile. It’s designed to include most of the object’s light while minimizing background contamination.\n",
+    "\n",
+    "The aperture is defined based on the Kron radius, which is calculated from the first moment of the light distribution. The resulting aperture is adaptive - it changes in size and shape depending on the morphology of the source."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b4f495d-1215-421d-bdb0-bc32fec92c25",
+   "metadata": {},
+   "source": [
+    "Define the coordinates and radius to use for the example queries in Sections 2 and 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "center_ra = 62\n",
+    "center_dec = -37\n",
+    "radius = 0.1\n",
+    "\n",
+    "str_center_coords = str(center_ra) + \", \" + str(center_dec)\n",
+    "str_radius = str(radius)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd80babb-ee05-49e9-9f9c-923d5c0cee31",
+   "metadata": {},
+   "source": [
+    "Start with the same query as used in the beginner TAP tutorial notebook 02a. Note that the extendedness flag in the $g-$band is used to select for galaxies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "985e3b62-8065-42ec-a40c-1232c4c45f17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, \"\\\n",
+    "        \"r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag \"\\\n",
+    "        \"FROM dp02_dc2_catalogs.Object \"\\\n",
+    "        \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \"\\\n",
+    "        \"CIRCLE('ICRS', \" + str_center_coords + \", \" + str_radius + \")) = 1 \"\\\n",
+    "        \"AND detect_isPrimary = 1 AND g_extendedness = 1\"\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f024085b-0f7f-45c6-8184-41b528c15396",
+   "metadata": {},
+   "source": [
+    "Run the query job asynchronously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = service.submit_job(query)\n",
+    "job.run()\n",
+    "job.wait(phases=[\"COMPLETED\", \"ERROR\"])\n",
+    "print(\"Job phase is\", job.phase)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80b28a39-bd12-49d6-9cce-f8ddfc31296c",
+   "metadata": {},
+   "source": [
+    "# 3. Explore the data using a `pandas` DataFrame object.\n",
+    "From the `pandas` docs:\n",
+    "> A DataFrame is a two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes (rows and columns)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07d1cfb1-589b-402b-8b8f-c2c70652b6c6",
+   "metadata": {},
+   "source": [
+    "Return the results as a `pandas` DataFrame, and then delete the query to save space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = job.fetch_result().to_table().to_pandas()\n",
+    "job.delete()\n",
+    "del query"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27888222-8fca-4620-a838-9260b0f5f47f",
+   "metadata": {},
+   "source": [
+    "Display `results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c59c4e8-90bd-4aa8-8ce2-9a14c09988a0",
+   "metadata": {},
+   "source": [
+    "There's a lot of options for investigating DataFrame objects. Some options are inspection- and summary-oriented, such as the `.head()`, `.tail()`, and `.describe()` attributes.\n",
+    "\n",
+    "Check these out now. `.head()` and `.tail()` show the first and last five rows, respectively, but can be modified to print out a different number of rows. `.describe()` provides statistics of the distribution of values in each column, including the mean and standard deviation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(results.head())\n",
+    "print(results.tail(10))\n",
+    "print(results.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b37fc18-e00b-4ef2-8d18-85d823d60d9e",
+   "metadata": {},
+   "source": [
+    "# 4. Visualize the data using `seaborn`\n",
+    "Use the boxplot tool from `seaborn` to visualize the distribution of the values in each column of the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(data=results)\n",
+    "plt.title('Box Plot of Data Distributions')\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Value')\n",
+    "plt.xticks(rotation=90);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a1199ac-f06b-46be-8a98-9b6ca175eb8f",
+   "metadata": {},
+   "source": [
+    "> A boxplot of the following columns: `coord_ra`, `coord_dec`, `g_kronFlux`, `g_kronflux_flag`, `r_kronFlux`, `r_kronFlux_flag`, `i_kronFlux`, and `i_kronFlux_flag`. The scaling only allows us to see the outlier circles for all of these columns, not the actual boxplot. This will need to be rescaled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99",
+   "metadata": {},
+   "source": [
+    "The outliers (points far from the majority of the distribution) are dominant in the visualization. Hide these and also only plot the Kron Flux values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(data=results[['g_kronFlux','r_kronFlux','i_kronFlux']], showfliers=False)\n",
+    "plt.title('Box Plot of Data Distributions')\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Value')\n",
+    "plt.xticks(rotation=90);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22a861da-38af-47c9-a29a-782fea694615",
+   "metadata": {},
+   "source": [
+    "> A boxplot of the following columns: `g_kronFlux`, `r_kronFlux`, and `i_kronFlux`. The boxplots have greater weight to lower values (<1000), with the whiskers extending for all columns to negative values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75d6336b-1068-46cf-8105-b945fd18020e",
+   "metadata": {},
+   "source": [
+    "Boxplots show a box and whiskers.\n",
+    "- The \"box\" is the interquartile range (IQR), which is the 25th percentile of the distribution of a value to the 75th percentile.\n",
+    "- The horizontal line inside the box is the median of the distribution.\n",
+    "- The whisker extends from the IQR to 1.5*IQR away from the edge of the box.\n",
+    "- Points outside the whisker are considered outliers (hidden here).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "448bec6a-15e1-49b5-a52e-bdd743ff207a",
+   "metadata": {},
+   "source": [
+    "Use `seaborn`'s violinplot tool to visualize the distribution for these same Kron flux values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "filtered_results = results[['g_kronFlux', 'r_kronFlux', 'i_kronFlux']].apply(lambda x: x[(x > x.quantile(0.05)) & (x < x.quantile(0.95))])\n",
+    "sns.violinplot(data=filtered_results,\n",
+    "              cut=0)\n",
+    "plt.title('Box Plot of Data Distributions')\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Value')\n",
+    "plt.xticks(rotation=90);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e2aa0ec-7ea6-4d65-94ee-b44ef7a673b6",
+   "metadata": {},
+   "source": [
+    "> A violinplot of the following columns: `g_kronFlux`, `r_kronFlux`, and `i_kronFlux`. The violinplots have greater weight to lower values (<1000), and also include boxplots inside."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f2c50a8-098e-4230-b122-3880c8bb1883",
+   "metadata": {},
+   "source": [
+    "A violinplot gives a lot of the same information as a boxplot, in fact, there are little boxplots within the violinplot; the horizontal white line is the median, the thicker grey box is the IQR and the thin line shows the 1.5*IQR span. A violinplot also uses a kernel density extimator to visualize the distribution of each feature. These plots reveal that most of the data are clustered around relatively low values for all of the Kron fluxes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ec470a9-8db0-403a-89ba-32e0dd9bef15",
+   "metadata": {},
+   "source": [
+    "# 5. Clean the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18dba188-ea4c-47e9-8faf-62e3552add1e",
+   "metadata": {},
+   "source": [
+    "Use `pandas` to investigate if there are any flags on the `kronFlux` measurement. The `.value_counts()` method will show the number of True and False columns, where True are rows for which the `g_kronFlux` measurement was flagged for a variety of reasons. There are many other columns that investigate specific reasons why this measurement is untrustworthy; the `g_kronFlux_flag` is a way to combine all of the individual flags. When this flag is set to `True`, the row is flagged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0be4535d-cc89-45ef-98e9-591b9f459fae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results['g_kronFlux_flag'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e94c30-66b3-4b9c-b79b-bcf024d5f214",
+   "metadata": {},
+   "source": [
+    "Okay what about the `r_kronFlux` measurement?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results['r_kronFlux_flag'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa7a4ce1-7bd4-47f8-a480-188b2c70579a",
+   "metadata": {},
+   "source": [
+    "Perform an intersection to see if the flagged entries overlap between these two photometric bands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06786c33-2563-4237-9d0f-22d6308c0d7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_values = set(results['r_kronFlux_flag'].unique())\n",
+    "g_values = set(results['g_kronFlux_flag'].unique())\n",
+    "\n",
+    "overlap = r_values & g_values\n",
+    "\n",
+    "overlap_true_rows = results[\n",
+    "    (results['r_kronFlux_flag'].isin(overlap)) & \n",
+    "    (results['g_kronFlux_flag'].isin(overlap)) & \n",
+    "    (results['r_kronFlux_flag'] == True) & \n",
+    "    (results['g_kronFlux_flag'] == True)\n",
+    "]\n",
+    "\n",
+    "print(overlap_true_rows)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e",
+   "metadata": {},
+   "source": [
+    "There are many overlapping rows, meaning that in these cases, both photometric bands are flagged. Since the task at hand is a prediction one between three bands ($g$, $r$, and $i$) the bigger concern is the cases where any of these individual three Kron fluxes are flagged. Exclude rows where this is the case and build an \"unflagged\" DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6294681-9c60-4ec6-805c-d378300acaa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unflagged_df = results[\n",
+    "    (results['r_kronFlux_flag'] == False) & \n",
+    "    (results['g_kronFlux_flag'] == False) &\n",
+    "    (results['i_kronFlux_flag'] == False)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38a84cda-eedb-4238-9d27-f91cb9f56531",
+   "metadata": {},
+   "source": [
+    "Visualize the relationship between $g$ and $r$ in this unflagged DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61a66274-c3e1-4e41-b743-649fc00d69b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(unflagged_df['g_kronFlux'], unflagged_df['r_kronFlux'])\n",
+    "plt.xlabel(r'$g-$band kronFlux [nJy]')\n",
+    "plt.ylabel(r'$r-$band kronFlux [nJy]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae55eb3a-8a47-44e5-8242-38467b215cde",
+   "metadata": {},
+   "source": [
+    "> Scatter plot of $g-$gand kronFlux [nJy] (x-axis) versus $r-$band kronFlux [nJy] (y-axis). The blue points appear to be roungly linear in this space with more concentration towards lower values and spread at higher values. The few points at high value have values on order 1e6 and there is a concentration around values less than 0.5e5 (in both axes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b76242e-6a0d-4f0c-ac35-fa27d4fef24a",
+   "metadata": {},
+   "source": [
+    "Zoom in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(unflagged_df['g_kronFlux'], unflagged_df['r_kronFlux'])\n",
+    "plt.xlabel(r'$g-$band kronFlux [nJy]')\n",
+    "plt.ylabel(r'$r-$band kronFlux [nJy]')\n",
+    "plt.xlim([0,30000])\n",
+    "plt.ylim([0,0.5e5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efffb2cf-1874-42f3-a646-fbe8753774c6",
+   "metadata": {},
+   "source": [
+    "> Scatter plot of $g-$gand kronFlux [nJy] (x-axis) versus $r-$band kronFlux [nJy] (y-axis). The blue points appear to be roungly linear in this space with more concentration towards lower values and spread at higher values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f503394-3816-4d31-9cf0-9de88e229f87",
+   "metadata": {},
+   "source": [
+    "There does seem to be a relationship between $g-$ and $r-$band Kron fluxes, meaning that it should be possible to do some predictive work here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca8c22a0-485a-4525-af2f-1e4c8bf74cd0",
+   "metadata": {},
+   "source": [
+    "Another thing to check before starting to fit is if there are any nans in these Series. (A Series is a single column of a DataFrame.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01fcf2a8-7f85-4ec0-8ebe-b69b76da7294",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(unflagged_df['g_kronFlux'].isna().any(), unflagged_df['r_kronFlux'].isna().any())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62479245-028e-4709-9c46-bc244463176c",
+   "metadata": {},
+   "source": [
+    "Both outputs should be `False`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2309022c-3530-4f3c-9f7a-7d51f3537afe",
+   "metadata": {},
+   "source": [
+    "Another important step is to check for negative (or zero) values in these data; they are fluxes so they should not be negative or equal to zero. First, count the number of these values in each of the $g-$ and $r-$band Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47a125c4-77fa-4712-be40-241318966774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neg_g = (unflagged_df['g_kronFlux'] <= 0).sum()\n",
+    "neg_r = (unflagged_df['r_kronFlux'] <= 0).sum()\n",
+    "\n",
+    "print(f\"Negative or zero values in g_kronFlux: {neg_g}\")\n",
+    "print(f\"Negative or zero values in r_kronFlux: {neg_r}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5d982a-939d-4e84-afbb-abe48975d502",
+   "metadata": {},
+   "source": [
+    "Uh oh! This is a big problem, let's mask all rows of the dataframe where any individual flux measurement is negative, making a \"clean\" DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffbe5670-6de4-4a92-99f7-4e480789b596",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = (unflagged_df['g_kronFlux'] > 0) & (unflagged_df['r_kronFlux'] > 0) & (unflagged_df['i_kronFlux'] > 0)\n",
+    "clean_df = unflagged_df[mask]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20799ed0-9d75-4a85-8a7c-57cebbda85f6",
+   "metadata": {},
+   "source": [
+    "It's always good to double check if the above mask worked. Below, there should be zero values in the `clean_df`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "782e7e2e-372e-4fcb-b837-a19a3bc83511",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neg_g = (clean_df['g_kronFlux'] <= 0).sum()\n",
+    "neg_r = (clean_df['r_kronFlux'] <= 0).sum()\n",
+    "print(f\"Negative or zero values in g_kronFlux: {neg_g}\")\n",
+    "print(f\"Negative or zero values in r_kronFlux: {neg_r}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828",
+   "metadata": {},
+   "source": [
+    "# 6. Prepare the training and test sets\n",
+    "The goal is to predict the $r-$band Kron flux using the $g-$band Kron flux. The first step is to define the training and validation data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    clean_df['g_kronFlux'].to_frame(),\n",
+    "    clean_df['r_kronFlux'].to_frame(),\n",
+    "    test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37d77db2-1a91-4696-831f-d426f3ca7539",
+   "metadata": {},
+   "source": [
+    "The `.to_frame()` argument is required to input the X data as a 2D shape, as expected by `scikit-learn`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecb487df-ce82-4d2d-9821-64620e1e922b",
+   "metadata": {},
+   "source": [
+    "It's best practice to use a standard scaler when training machine learning models. Transform the training and test data, scaling to a mean of zero and a standard deviation of one by default.\n",
+    "\n",
+    "**Note to future developer of this notebook: I (Becky) was wondering if a log transform is also necessary because the data seems logarithmically scaled. I'm not sure if it's needed, but I've set this up to also include an invertible log transform if you want to test it.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f09a28c9-f868-4cfa-a309-c53b26193e01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_scaler = FunctionTransformer(func=np.log, inverse_func=np.exp, validate=True)\n",
+    "\n",
+    "X_scaler = make_pipeline(\n",
+    "    #log_scaler,\n",
+    "    StandardScaler()\n",
+    ")\n",
+    "\n",
+    "y_scaler = make_pipeline(\n",
+    "    #log_scaler,\n",
+    "    StandardScaler()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c8aeb31-3cbd-461f-88de-3e60e9acb4a7",
+   "metadata": {},
+   "source": [
+    "Fit to the transform and then also transform the test data according to the scaler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec1efab7-be4c-4bdc-9ead-78fd6b400345",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_scaled = X_scaler.fit_transform(X_train)\n",
+    "X_test_scaled = X_scaler.transform(X_test)\n",
+    "\n",
+    "y_train_scaled = y_scaler.fit_transform(y_train)\n",
+    "y_test_scaled = y_scaler.transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f990ce31-fb46-4c64-b56c-b26781c8e571",
+   "metadata": {},
+   "source": [
+    "Check that the resultant distribution for training data has a center of roughly zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5be7b1b1-2177-46e5-ad41-607e55d12949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.clf()\n",
+    "plt.hist(X_train_scaled, range=[-2,2]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e0d7b9a-b383-43c2-bb4a-826586326484",
+   "metadata": {},
+   "source": [
+    "> A histogram with a x-axis range from -2 to 2 for the distribution of the rescaled `X_train` data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dfc5412-e4a5-4f32-a27c-25acb1a8c54c",
+   "metadata": {},
+   "source": [
+    "Perform the inverse transform to check if the twice transformed data is the same as the original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4086f5f-7c2d-4d6c-a2d4-8dff42e2fc84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_tt = X_scaler.inverse_transform(X_train_scaled)\n",
+    "X_test_tt = X_scaler.inverse_transform(X_test_scaled)\n",
+    "\n",
+    "y_train_tt = y_scaler.inverse_transform(y_train_scaled)\n",
+    "y_test_tt = y_scaler.inverse_transform(y_test_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46ca963a-c142-438b-91a7-c6e30904e45e",
+   "metadata": {},
+   "source": [
+    "Check that the original `X_train` is the same as the twice transformed `X_train_tt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc4cecc6-f256-4f55-8d54-f585630e8b4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.clf()\n",
+    "plt.hist(X_train_tt, range=[0,1500], label='twice transformed', alpha=0.5, color='red')\n",
+    "plt.hist(X_train, range=[0,1500], label='original', alpha=0.5, color='orange')\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad501069-4509-40d2-b05d-03d800e40296",
+   "metadata": {},
+   "source": [
+    "> Overlapping histograms for `X_train` and `X_train_tt` with an x-axis range of 0 to 1500."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28",
+   "metadata": {},
+   "source": [
+    "# 7. Model (using `scikit-learn`)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48c8d648-288f-4635-a961-248f127fe524",
+   "metadata": {},
+   "source": [
+    "## 7.1 Start with a linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b99ff86e-f364-4edb-8c4f-bdd645a525ae",
+   "metadata": {},
+   "source": [
+    "Instantiate the model and then fit it with the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e02ca479-6105-442b-9879-2eb215dc4d66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_lr = LinearRegression()\n",
+    "model_lr.fit(X_train_scaled, y_train_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2fe11b4-a9ad-45f9-9c1e-e25bb3a0301c",
+   "metadata": {},
+   "source": [
+    "Now predict the test data. Compare the prediction to the true value. Use the mean squared error function to get a diagnostic on how well the fit is behaving for the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aca8064a-c94f-4792-86b3-62ec2130471b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model_lr.predict(X_test_scaled)\n",
+    "mse = mean_squared_error(y_test_scaled, y_pred)\n",
+    "print(\"MSE:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9086695-43c6-49e5-a751-bcabc275172d",
+   "metadata": {},
+   "source": [
+    "Plot how the predicted values compare to the true values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee8bd887-928e-4a41-bd77-149b344ab238",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.clf()\n",
+    "plt.scatter(y_test_scaled, y_pred)\n",
+    "plt.plot([-4,6],[-4,6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([-5,5])\n",
+    "plt.ylim([-5,5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f33c5476-7662-4e5d-95e5-a2103e6ab2d8",
+   "metadata": {},
+   "source": [
+    "> Scatter plot of true versus predicted value for the scaled $r-$band Kron flux. A 1:1 line shows the expected value if the linear regression were a perfect predictor."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8367d3db-3527-4cab-abdb-51391259ec02",
+   "metadata": {},
+   "source": [
+    "Transform the predictions back to the true values (remember the current scaling is standardized) and look at the results in that space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6770c381-1f34-44a7-b93b-b7f915013620",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_tt = y_scaler.inverse_transform(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f83cf984-ffa7-4486-98ec-bce890b91bad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.clf()\n",
+    "plt.scatter(y_test_tt, y_pred_tt)\n",
+    "plt.plot([-1e3,2e4],[-1e3,2e4], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted');\n",
+    "plt.xlim([-1e3,2e4])\n",
+    "plt.ylim([-1e3,2e4]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2b12a25-236e-4a1b-804b-03b97960712c",
+   "metadata": {},
+   "source": [
+    "> A scatterplot of the true versus predicted value for the $r-$band Kron flux scaled back to the original values. The x- and y-axes scale between -1000 to 2e4."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da4c11bc-53bb-48b5-a89f-aeb2628fd77d",
+   "metadata": {},
+   "source": [
+    "Also get the MSE for the rescaled y value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb653dbd-4e77-4099-80ac-781f20cb38eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mse = mean_squared_error(y_test_tt, y_pred_tt)\n",
+    "print(\"MSE:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1de26eb9-e7cf-4e94-bf14-0fda5d8efe18",
+   "metadata": {},
+   "source": [
+    "## 7.2 Improve the model with more features\n",
+    "Let's see if this will improve with more predictive values, this time including the $i-$band Kron flux. Split the training/test sets, perform the standard scaling, and linear regression. This time, do not scale the y-value $r-$band Kron flux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_i, X_test_i, y_train_i, y_test_i = train_test_split(\n",
+    "    clean_df[['g_kronFlux', 'i_kronFlux']],\n",
+    "    clean_df['r_kronFlux'],\n",
+    "    test_size=0.2,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_i_scaled = scaler.fit_transform(X_train_i)\n",
+    "X_test_i_scaled = scaler.transform(X_test_i)\n",
+    "\n",
+    "model_mlr = LinearRegression()\n",
+    "model_mlr.fit(X_train_i_scaled, y_train_i)\n",
+    "\n",
+    "y_pred_i = model_mlr.predict(X_test_i_scaled)\n",
+    "\n",
+    "mse = mean_squared_error(y_test_i, y_pred_i)\n",
+    "print(\"MSE:\", mse)\n",
+    "\n",
+    "plt.clf()\n",
+    "plt.scatter(y_test_i, y_pred_i)\n",
+    "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0, 2e4])\n",
+    "plt.ylim([0, 2e4]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f72c9ad-547a-455a-8b0e-39674e94fe43",
+   "metadata": {},
+   "source": [
+    "> A scatter plot demonstrating a tighter fit when the $i-$band Kron flux is included as a predictive feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30cd4c78-35e5-4459-84bf-9642068da002",
+   "metadata": {},
+   "source": [
+    "Test for the reader: try to improve this further by including more features. The MSE has already improved from the above value!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89e92033-ff41-4662-b5aa-ece41c216a07",
+   "metadata": {},
+   "source": [
+    "## 7.3 Random forest regressor\n",
+    "These are a great type of model for regression or classification. Use it here to perform regression, again, also including the $i-$band Kron fluxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb56f9d-6487-444d-b283-6d60c1694948",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_rf = RandomForestRegressor()\n",
+    "model_rf.fit(X_train_i_scaled, y_train_i)\n",
+    "\n",
+    "y_pred_i_rf = model_rf.predict(X_test_i_scaled)\n",
+    "\n",
+    "\n",
+    "mse = mean_squared_error(y_test_i, y_pred_i_rf)\n",
+    "print(\"MSE:\", mse)\n",
+    "\n",
+    "plt.clf()\n",
+    "plt.scatter(y_test_i, y_pred_i_rf)\n",
+    "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0, 2e4])\n",
+    "plt.ylim([0, 2e4]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b186b9-80b7-455e-8433-78c531050100",
+   "metadata": {},
+   "source": [
+    "> Another scatterplot of true versus predicted y-values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13845e03-08fe-4f80-8883-a671e8afd9d5",
+   "metadata": {},
+   "source": [
+    "**Strange that the MSE is actually worse than linear regression.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed0b4e6-904a-4af8-8bfe-cad402de09a4",
+   "metadata": {},
+   "source": [
+    "# 8. Hyperparameter tuning\n",
+    "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance. Define the grid to search over, here test the `n_estimators` parameter from random forest estimators, which defines the number of trees in the random forest. The default setting is 100."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6420904-44bb-40cf-8ce0-92b22a802e59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e337134d-22fe-42d3-83d2-9f22bb93e70e",
+   "metadata": {},
+   "source": [
+    "Now define the `GridSearchCV` object and fit the model, wrapped in this grid search object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48a166c2-10b9-470f-bd03-3d7e6d51a1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid = GridSearchCV(model_rf, param_grid, cv=5)\n",
+    "\n",
+    "grid.fit(X_train_i_scaled, y_train_i)\n",
+    "\n",
+    "print(grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8445ad22-4275-4a56-ace7-ce1a678dd900",
+   "metadata": {},
+   "source": [
+    "Now retrieve the best model and print out model diagnostics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = grid.best_estimator_\n",
+    "print(\"Best Model:\", best_model)\n",
+    "y_pred_grid = best_model.predict(X_test_i_scaled)\n",
+    "plt.clf()\n",
+    "plt.scatter(y_test_i, y_pred_grid)\n",
+    "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0, 2e4])\n",
+    "plt.ylim([0, 2e4])\n",
+    "plt.show()\n",
+    "\n",
+    "mse = mean_squared_error(y_test_i, y_pred_grid)\n",
+    "print(\"MSE:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6438d53-e03b-4afa-a417-489064caf51e",
+   "metadata": {},
+   "source": [
+    "**To do: The multiple linear regression currently has a better MSE than this model, investigate why that is a little more.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec3e2b39-f86e-4dfa-a97a-9e018fc2208c",
+   "metadata": {},
+   "source": [
+    "# 9. Fit the best model to rows where the $r-$band Kron flux is missing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98f817da-d4cd-40d2-be10-ba9c1669e68e",
+   "metadata": {},
+   "source": [
+    "First, return to the dataframe and snag rows where the $r-$band flux is flagged but the $g-$ and $i-$band Kron fluxes are intact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a020b226-289d-4587-9e42-47f64574dbf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_missing_df = results[\n",
+    "    (results['r_kronFlux_flag'] == True) & \n",
+    "    (results['g_kronFlux_flag'] == False) &\n",
+    "    (results['i_kronFlux_flag'] == False)\n",
+    "]\n",
+    "r_missing_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd7a891e-5a56-440e-a721-91af73d74269",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_r_missing = r_missing_df[['g_kronFlux', 'i_kronFlux']]\n",
+    "X_r_missing_scaled = scaler.fit_transform(X_r_missing)\n",
+    "y_pred_r_missing = best_model.predict(X_r_missing_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e438d24-75ba-4888-b2c9-cb4fa6c6db85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(r_missing_df['r_kronFlux'].values, y_pred_r_missing)\n",
+    "plt.xlabel(r'Flagged $r-$band values')\n",
+    "plt.ylabel('Predicted $r-$band values');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4c11515-3263-4d8e-bf08-fa60f7cafb9d",
+   "metadata": {},
+   "source": [
+    "> A scatterplot of flagged (original) $r-$band Kron flux values against the predicted values from the best fit model. The spread is much greater in the y-axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c59d12-a11f-40b7-b7fb-84276d0c7c78",
+   "metadata": {},
+   "source": [
+    "**To do: Also plot against the predictors ($g-$ and $i-$band) Kron fluxes for a better idea of how this model performs.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8",
+   "metadata": {},
+   "source": [
+    "# 10. Other available `scikit-learn` choices\n",
+    "The below two cells explore available options from `scikit-learn` for regression metrics and regression models, respectively. The metric cell is truncated with a `break` statement to only print details of the first metric. The model cell demonstrates printing the class information for the `RandomForestRegressor` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regression_metrics = [\n",
+    "    name for name, obj in inspect.getmembers(metrics)\n",
+    "    if inspect.isfunction(obj)\n",
+    "    and ('regression' in (obj.__doc__ or '').lower() or 'error' in (obj.__doc__ or '').lower())\n",
+    "    and 'classification' not in (obj.__doc__ or '').lower()\n",
+    "]\n",
+    "print(regression_metrics)\n",
+    "\n",
+    "\n",
+    "# Print the filtered metrics and their documentation\n",
+    "for metric in regression_metrics:\n",
+    "    metric_func = getattr(metrics, metric)\n",
+    "    if metric == \"mean_tweedie_deviance\":\n",
+    "        print(f\"--- {metric} ---\")\n",
+    "        help(metric_func)\n",
+    "        print(\"=\"*80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de4a41f7-9f4f-4662-9302-2da7d68df434",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all regression models\n",
+    "regressors = all_estimators(type_filter='regressor')\n",
+    "\n",
+    "# Print the names of all available regression models\n",
+    "for name, estimator in regressors:\n",
+    "    print(name)\n",
+    "\n",
+    "for name, estimator in regressors:\n",
+    "    if name == \"RandomForestRegressor\":\n",
+    "        print(help(estimator))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e205cc2-26f1-4b51-bb36-3ce2ed8cece4",
+   "metadata": {},
+   "source": [
+    "# Exercise for the learner\n",
+    "\n",
+    "The uncertainty values are also available for each column of Kron fluxes. Use those to run an uncertainty-aware model fit for the $r-$band Kron fluxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7877e1b2-e1c8-4d1f-a2b8-60c758cdf61d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "LSST",
+   "language": "python",
+   "name": "lsst"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb b/DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb
new file mode 100644
index 00000000..6232a288
--- /dev/null
+++ b/DP0.2/20_Introduction_to_Data_Science_with_Rubin_Data.ipynb
@@ -0,0 +1,2910 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "90083a24-00a4-4a6f-a1c3-b9b4c6b0de9e.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6c298cbb-eb18-4cbc-b064-709a7c2b9b4c",
+   "metadata": {},
+   "source": [
+    "# XXX.X. How to use `pandas` and `scikit-learn` to do data science / machine learning with the TAP service\n",
+    "\n",
+    "<div>\n",
+    "<img align=\"left\" src=\"attachment:90083a24-00a4-4a6f-a1c3-b9b4c6b0de9e.png\" width=\"300\" style=\"padding-right: 20px;\"/>\n",
+    "</div>\n",
+    "For the Rubin Science Platform at data.lsst.cloud. <br>\n",
+    "Data Release: <i>DPX or DRX</i> <br>\n",
+    "Container Size: small <br>\n",
+    "LSST Science Pipelines version: Weekly 2024_16 <br>\n",
+    "Last verified to run: 2024-07-09 <br>\n",
+    "Repository: <a href=\"https://github.com/lsst/tutorial-notebooks\">github.com/lsst/tutorial-notebooks</a> <br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a4e60f3-a825-4dcc-8cd5-37ed43b7373b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext pycodestyle_magic\n",
+    "%flake8_on\n",
+    "import logging\n",
+    "logging.getLogger(\"flake8\").setLevel(logging.FATAL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25e71641-fbfb-4470-8049-03ba631dbef1",
+   "metadata": {},
+   "source": [
+    "**Learning objective:** This notebook guides a PI through the process of using python's data science and machine learning libraries to explore data from complex ADQL queries with the TAP service. The goal is to build a predictive model to estimate missing $r-$band Kron Flux values when the other bands are available, and visualize the results and quantify the model performance.\n",
+    "\n",
+    "**LSST data products:** Object, Forcedsource, and CcdVisit tables.\n",
+    "\n",
+    "**Packages:** lsst.rsp, pandas, scikit-learn, seaborn\n",
+    "\n",
+    "**Credit:**\n",
+    "Based on notebooks developed by Leanne Guy (TAP query) and Alex Drlica-Wagner and Melissa Graham (Butler query).\n",
+    "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n",
+    "\n",
+    "**Get Support:**\n",
+    "Everyone is encouraged to ask questions or raise issues in the \n",
+    "<a href=\"https://community.lsst.org/c/support\">Support Category</a> \n",
+    "of the Rubin Community Forum.\n",
+    "Rubin staff will respond to all questions posted there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca5378d1-00fe-44ad-be88-5f1a0a85b404",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction\n",
+    "\n",
+    "This notebook provides an intermediate-level demonstration of how to use the Table Access Protocol (TAP) server and ADQL (Astronomy Data Query Language) to query and retrieve data from the DP0.2 catalogs.\n",
+    "\n",
+    "TAP provides standardized access to catalog data for discovery, search, and retrieval.\n",
+    "Full <a href=\"http://www.ivoa.net/documents/TAP\">documentation for TAP</a> is provided by the International Virtual Observatory Alliance (IVOA).\n",
+    "ADQL is similar to SQL (Structured Query Langage).\n",
+    "The <a href=\"http://www.ivoa.net/documents/latest/ADQL.html\">documentation for ADQL</a> includes more information about syntax and keywords.\n",
+    "Note that not all ADQL functionality is supported yet in the DP0-era RSP.\n",
+    "\n",
+    "**See the recommendations for TAP queries in DP0.2 tutorial 02a \"Introduction to the TAP Service\".**\n",
+    "\n",
+    "The [documentation for Data Preview 0.2](https://dp0-2.lsst.io/) includes definitions\n",
+    "of the data products, descriptions of catalog contents, and ADQL recipes.\n",
+    "\n",
+    "### 1.1. Package imports\n",
+    "\n",
+    "Import general python packages, the Rubin TAP service utilities, and various scikit-learn utilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3f4900a4-3358-472a-b9ba-c42e3f2f0771",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:16:10.447704Z",
+     "iopub.status.busy": "2024-12-03T18:16:10.447270Z",
+     "iopub.status.idle": "2024-12-03T18:16:11.741424Z",
+     "shell.execute_reply": "2024-12-03T18:16:11.740792Z",
+     "shell.execute_reply.started": "2024-12-03T18:16:10.447676Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas\n",
+    "\n",
+    "from astropy import units as u\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "from lsst.rsp import get_tap_service, retrieve_query"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90251edc-e77a-4f2c-aef3-935381faebc9",
+   "metadata": {},
+   "source": [
+    "Set up seaborn to use 538's aesthetics. This is probably not what we want to the rtn-045 default plotting settings though..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "94acc9f6-2033-4ace-aefd-d036a35f4221",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:18:25.800083Z",
+     "iopub.status.busy": "2024-12-03T18:18:25.799778Z",
+     "iopub.status.idle": "2024-12-03T18:18:25.803998Z",
+     "shell.execute_reply": "2024-12-03T18:18:25.803416Z",
+     "shell.execute_reply.started": "2024-12-03T18:18:25.800052Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "palette = sns.color_palette(\"muted\")  # Choose a desired palette\n",
+    "sns.set_palette(palette)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca1e28f4-805e-4480-a7c6-0473b7e2b088",
+   "metadata": {},
+   "source": [
+    "### 1.2. Define functions and parameters\n",
+    "\n",
+    "Instantiate the TAP service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "caf56589-100a-4481-8f24-5f5058b6671f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:18:31.983738Z",
+     "iopub.status.busy": "2024-12-03T18:18:31.983012Z",
+     "iopub.status.idle": "2024-12-03T18:18:32.026282Z",
+     "shell.execute_reply": "2024-12-03T18:18:32.025768Z",
+     "shell.execute_reply.started": "2024-12-03T18:18:31.983710Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "service = get_tap_service(\"tap\")\n",
+    "assert service is not None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eb0f20e-6c28-404f-8032-acc00f73405a",
+   "metadata": {},
+   "source": [
+    "Set the maximum number of rows to display from pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2b7b6002-2457-4c20-a03e-6bfa24a0aa27",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:18:32.908017Z",
+     "iopub.status.busy": "2024-12-03T18:18:32.907315Z",
+     "iopub.status.idle": "2024-12-03T18:18:32.910925Z",
+     "shell.execute_reply": "2024-12-03T18:18:32.910377Z",
+     "shell.execute_reply.started": "2024-12-03T18:18:32.907992Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pandas.set_option('display.max_rows', 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd325fe4-6c7c-4803-ad79-f30d7edc23e3",
+   "metadata": {},
+   "source": [
+    "## 2. Query for Kron fluxes around extended (galaxy) objects.\n",
+    "I forget why I chose this specific statistic for the demo.\n",
+    "\n",
+    "Kron radius: A radius that is calculated using the light profile of the object, typically as the first moment (i.e., a weighted average of radius with brightness) of the light distribution.\n",
+    "\n",
+    "Kron flux: The total flux measured within a certain multiple (often 2.5×) of the Kron radius, typically capturing about 90–95% of the total light for extended sources like galaxies."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b4f495d-1215-421d-bdb0-bc32fec92c25",
+   "metadata": {},
+   "source": [
+    "Define the coordinates and radius to use for the example queries in Sections 2 and 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7ddd0344-b354-45a0-9e5a-755149c9bc54",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:18:35.079591Z",
+     "iopub.status.busy": "2024-12-03T18:18:35.078853Z",
+     "iopub.status.idle": "2024-12-03T18:18:35.082610Z",
+     "shell.execute_reply": "2024-12-03T18:18:35.082033Z",
+     "shell.execute_reply.started": "2024-12-03T18:18:35.079566Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "center_ra = 62\n",
+    "center_dec = -37\n",
+    "radius = 0.1\n",
+    "\n",
+    "str_center_coords = str(center_ra) + \", \" + str(center_dec)\n",
+    "str_radius = str(radius)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd80babb-ee05-49e9-9f9c-923d5c0cee31",
+   "metadata": {},
+   "source": [
+    "Start with the same query as used in the beginner TAP tutorial notebook 02a."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "985e3b62-8065-42ec-a40c-1232c4c45f17",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:19:13.171378Z",
+     "iopub.status.busy": "2024-12-03T18:19:13.170670Z",
+     "iopub.status.idle": "2024-12-03T18:19:13.174956Z",
+     "shell.execute_reply": "2024-12-03T18:19:13.174323Z",
+     "shell.execute_reply.started": "2024-12-03T18:19:13.171349Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag FROM dp02_dc2_catalogs.Object WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), CIRCLE('ICRS', 62, -37, 0.1)) = 1 AND detect_isPrimary = 1 AND g_extendedness = 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"SELECT coord_ra, coord_dec, g_kronFlux, g_kronFlux_flag, \"\\\n",
+    "        \"r_kronFlux, r_kronFlux_flag, i_kronFlux, i_kronFlux_flag \"\\\n",
+    "        \"FROM dp02_dc2_catalogs.Object \"\\\n",
+    "        \"WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \"\\\n",
+    "        \"CIRCLE('ICRS', \" + str_center_coords + \", \" + str_radius + \")) = 1 \"\\\n",
+    "        \"AND detect_isPrimary = 1 AND g_extendedness = 1\"\n",
+    "print(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f024085b-0f7f-45c6-8184-41b528c15396",
+   "metadata": {},
+   "source": [
+    "Run the query job asynchronously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "c02adc91-5f5e-418b-87a3-cba8beba7dd2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:19:14.234845Z",
+     "iopub.status.busy": "2024-12-03T18:19:14.234550Z",
+     "iopub.status.idle": "2024-12-03T18:19:21.521239Z",
+     "shell.execute_reply": "2024-12-03T18:19:21.520430Z",
+     "shell.execute_reply.started": "2024-12-03T18:19:14.234825Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Job phase is COMPLETED\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = service.submit_job(query)\n",
+    "job.run()\n",
+    "job.wait(phases=['COMPLETED', 'ERROR'])\n",
+    "print('Job phase is', job.phase)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80b28a39-bd12-49d6-9cce-f8ddfc31296c",
+   "metadata": {},
+   "source": [
+    "## 3. Explore the data using a `pandas` DataFrame object.\n",
+    "DEFINE WHAT A DATAFRAME IS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07d1cfb1-589b-402b-8b8f-c2c70652b6c6",
+   "metadata": {},
+   "source": [
+    "Return the results as a `pandas` dataframe, and then delete the query to save space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "8cd2f538-c2d7-44ca-ab4d-825120b8f2e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:19:21.522743Z",
+     "iopub.status.busy": "2024-12-03T18:19:21.522437Z",
+     "iopub.status.idle": "2024-12-03T18:19:21.848448Z",
+     "shell.execute_reply": "2024-12-03T18:19:21.847864Z",
+     "shell.execute_reply.started": "2024-12-03T18:19:21.522716Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results = job.fetch_result().to_table().to_pandas()\n",
+    "job.delete()\n",
+    "del query"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27888222-8fca-4620-a838-9260b0f5f47f",
+   "metadata": {},
+   "source": [
+    "Display `results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ee4d121e-6b4d-4371-afae-4f7587b95d51",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:18:54.059294Z",
+     "iopub.status.busy": "2024-12-03T18:18:54.058632Z",
+     "iopub.status.idle": "2024-12-03T18:18:54.074345Z",
+     "shell.execute_reply": "2024-12-03T18:18:54.073785Z",
+     "shell.execute_reply.started": "2024-12-03T18:18:54.059270Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coord_ra</th>\n",
+       "      <th>coord_dec</th>\n",
+       "      <th>g_kronFlux</th>\n",
+       "      <th>g_kronFlux_flag</th>\n",
+       "      <th>r_kronFlux</th>\n",
+       "      <th>r_kronFlux_flag</th>\n",
+       "      <th>i_kronFlux</th>\n",
+       "      <th>i_kronFlux_flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>62.018897</td>\n",
+       "      <td>-37.095671</td>\n",
+       "      <td>71.568352</td>\n",
+       "      <td>True</td>\n",
+       "      <td>91.185588</td>\n",
+       "      <td>True</td>\n",
+       "      <td>624.454022</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62.020999</td>\n",
+       "      <td>-37.093227</td>\n",
+       "      <td>174.729861</td>\n",
+       "      <td>False</td>\n",
+       "      <td>110.922305</td>\n",
+       "      <td>False</td>\n",
+       "      <td>52.040203</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>62.000430</td>\n",
+       "      <td>-37.093196</td>\n",
+       "      <td>131.680920</td>\n",
+       "      <td>False</td>\n",
+       "      <td>137.655812</td>\n",
+       "      <td>False</td>\n",
+       "      <td>136.174616</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11561</th>\n",
+       "      <td>61.950427</td>\n",
+       "      <td>-36.946586</td>\n",
+       "      <td>51.054369</td>\n",
+       "      <td>True</td>\n",
+       "      <td>175.646973</td>\n",
+       "      <td>False</td>\n",
+       "      <td>123.073904</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11562</th>\n",
+       "      <td>61.976752</td>\n",
+       "      <td>-36.904225</td>\n",
+       "      <td>199.039503</td>\n",
+       "      <td>False</td>\n",
+       "      <td>187.972452</td>\n",
+       "      <td>False</td>\n",
+       "      <td>115.825734</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11563</th>\n",
+       "      <td>61.932319</td>\n",
+       "      <td>-36.941077</td>\n",
+       "      <td>266.123377</td>\n",
+       "      <td>False</td>\n",
+       "      <td>218.853195</td>\n",
+       "      <td>False</td>\n",
+       "      <td>481.650950</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11564 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        coord_ra  coord_dec  g_kronFlux  g_kronFlux_flag  r_kronFlux  \\\n",
+       "0      62.018897 -37.095671   71.568352             True   91.185588   \n",
+       "1      62.020999 -37.093227  174.729861            False  110.922305   \n",
+       "2      62.000430 -37.093196  131.680920            False  137.655812   \n",
+       "...          ...        ...         ...              ...         ...   \n",
+       "11561  61.950427 -36.946586   51.054369             True  175.646973   \n",
+       "11562  61.976752 -36.904225  199.039503            False  187.972452   \n",
+       "11563  61.932319 -36.941077  266.123377            False  218.853195   \n",
+       "\n",
+       "       r_kronFlux_flag  i_kronFlux  i_kronFlux_flag  \n",
+       "0                 True  624.454022             True  \n",
+       "1                False   52.040203             True  \n",
+       "2                False  136.174616             True  \n",
+       "...                ...         ...              ...  \n",
+       "11561            False  123.073904             True  \n",
+       "11562            False  115.825734            False  \n",
+       "11563            False  481.650950             True  \n",
+       "\n",
+       "[11564 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d493b9b-0aba-4586-bcd0-3e1ce1f09c16",
+   "metadata": {},
+   "source": [
+    "`results` is a `pandas` DataFrame object (see below). There's lots you can do with this type of object..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "db2168fe-593a-423d-b2f4-26ac0db60e8c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:19:48.071264Z",
+     "iopub.status.busy": "2024-12-03T18:19:48.070609Z",
+     "iopub.status.idle": "2024-12-03T18:19:48.074799Z",
+     "shell.execute_reply": "2024-12-03T18:19:48.074312Z",
+     "shell.execute_reply.started": "2024-12-03T18:19:48.071232Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c59c4e8-90bd-4aa8-8ce2-9a14c09988a0",
+   "metadata": {},
+   "source": [
+    "Some options are inspection- and summary-oriented, such as the `.head()`, `.tail()`, and `.describe()` attributes. Let's check these out now. `.head()` and `.tail()` give you the first and last five rows, respectively, but can be modified to print out a different number of rows. `.describe()` will provide statistics of the distribution of values in each column, including the mean and standard deviation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "eec25f58-d3f3-4ef4-b3e2-ab105c4718fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:23:39.207146Z",
+     "iopub.status.busy": "2024-12-03T18:23:39.206862Z",
+     "iopub.status.idle": "2024-12-03T18:23:39.230317Z",
+     "shell.execute_reply": "2024-12-03T18:23:39.229617Z",
+     "shell.execute_reply.started": "2024-12-03T18:23:39.207125Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    coord_ra  coord_dec  g_kronFlux  g_kronFlux_flag  r_kronFlux  \\\n",
+      "0  62.018897 -37.095671   71.568352             True   91.185588   \n",
+      "1  62.020999 -37.093227  174.729861            False  110.922305   \n",
+      "2  62.000430 -37.093196  131.680920            False  137.655812   \n",
+      "3  62.015568 -37.092868  372.665560            False  171.582869   \n",
+      "4  62.002969 -37.092762  247.219720            False  153.138653   \n",
+      "\n",
+      "   r_kronFlux_flag  i_kronFlux  i_kronFlux_flag  \n",
+      "0             True  624.454022             True  \n",
+      "1            False   52.040203             True  \n",
+      "2            False  136.174616             True  \n",
+      "3            False  211.338418             True  \n",
+      "4             True  184.829166             True  \n",
+      "        coord_ra  coord_dec  g_kronFlux  g_kronFlux_flag  r_kronFlux  \\\n",
+      "11554  61.913511 -36.960012  158.939682            False         NaN   \n",
+      "11555  61.986424 -36.950292  125.535210            False   71.749436   \n",
+      "11556  61.942562 -36.951137  108.966860            False  135.301872   \n",
+      "...          ...        ...         ...              ...         ...   \n",
+      "11561  61.950427 -36.946586   51.054369             True  175.646973   \n",
+      "11562  61.976752 -36.904225  199.039503            False  187.972452   \n",
+      "11563  61.932319 -36.941077  266.123377            False  218.853195   \n",
+      "\n",
+      "       r_kronFlux_flag  i_kronFlux  i_kronFlux_flag  \n",
+      "11554             True  102.094474             True  \n",
+      "11555             True         NaN             True  \n",
+      "11556            False  191.964068             True  \n",
+      "...                ...         ...              ...  \n",
+      "11561            False  123.073904             True  \n",
+      "11562            False  115.825734            False  \n",
+      "11563            False  481.650950             True  \n",
+      "\n",
+      "[10 rows x 8 columns]\n",
+      "           coord_ra     coord_dec     g_kronFlux    r_kronFlux    i_kronFlux\n",
+      "count  11564.000000  11564.000000   11447.000000  1.145300e+04  1.129200e+04\n",
+      "mean      61.999517    -37.001530     761.468963  1.368137e+03  2.071233e+03\n",
+      "std        0.062390      0.050868   13194.742739  2.395294e+04  3.258561e+04\n",
+      "...             ...           ...            ...           ...           ...\n",
+      "50%       61.999039    -37.001694     182.963268  2.409297e+02  3.517374e+02\n",
+      "75%       62.049891    -36.960187     340.536311  4.811457e+02  7.569082e+02\n",
+      "max       62.124349    -36.900195  782163.585831  1.957761e+06  2.870998e+06\n",
+      "\n",
+      "[8 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results.head())\n",
+    "print(results.tail(10))\n",
+    "print(results.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b37fc18-e00b-4ef2-8d18-85d823d60d9e",
+   "metadata": {},
+   "source": [
+    "## 4. Visualize using `seaborn`\n",
+    "Let's look further into visualizing these statistics using `seaborn`'s boxplot tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "4fc9b578-2be4-4fb2-8d74-ebca809ea99f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:55:30.244276Z",
+     "iopub.status.busy": "2024-12-03T18:55:30.243648Z",
+     "iopub.status.idle": "2024-12-03T18:55:30.835103Z",
+     "shell.execute_reply": "2024-12-03T18:55:30.834453Z",
+     "shell.execute_reply.started": "2024-12-03T18:55:30.244249Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(data=results)\n",
+    "plt.title('Box Plot of Data Distributions')\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Value')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef5f20de-0fd6-4ba1-9cab-9d59cd05df99",
+   "metadata": {},
+   "source": [
+    "It looks like outliers are dominant in the visualization. Hide these and also only plot the kron Flux values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "bacf5114-6a64-4100-8eb6-f1d9ddc36f89",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T18:59:02.839663Z",
+     "iopub.status.busy": "2024-12-03T18:59:02.839254Z",
+     "iopub.status.idle": "2024-12-03T18:59:03.091807Z",
+     "shell.execute_reply": "2024-12-03T18:59:03.091120Z",
+     "shell.execute_reply.started": "2024-12-03T18:59:02.839637Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(data=results[['g_kronFlux','r_kronFlux','i_kronFlux']], showfliers=False)\n",
+    "plt.title('Box Plot of Data Distributions')\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Value')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75d6336b-1068-46cf-8105-b945fd18020e",
+   "metadata": {},
+   "source": [
+    "Boxplots show a box and whiskers.\n",
+    "- The \"box\" is the interquartile range (IQR), which is the 25th percentile of the distribution of a value to the 75th percentile.\n",
+    "- The horizontal line inside the box is the median of the distribution.\n",
+    "- The whisker extends from the IQR to 1.5*IQR away from the edge of the box.\n",
+    "- Points outside the whisker are considered outliers (hidden here).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "39521ac6-0bec-42e7-9062-8fc9ce5edc55",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T19:11:16.903575Z",
+     "iopub.status.busy": "2024-12-03T19:11:16.902993Z",
+     "iopub.status.idle": "2024-12-03T19:11:17.202209Z",
+     "shell.execute_reply": "2024-12-03T19:11:17.201547Z",
+     "shell.execute_reply.started": "2024-12-03T19:11:16.903550Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_638/2438356921.py:3: FutureWarning: \n",
+      "\n",
+      "The `bw` parameter is deprecated in favor of `bw_method`/`bw_adjust`.\n",
+      "Setting `bw_method=0.2`, but please see docs for the new parameters\n",
+      "and update your code. This will become an error in seaborn v0.15.0.\n",
+      "\n",
+      "  sns.violinplot(data=filtered_results,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "filtered_results = results[['g_kronFlux', 'r_kronFlux', 'i_kronFlux']].apply(lambda x: x[(x > x.quantile(0.05)) & (x < x.quantile(0.95))])\n",
+    "sns.violinplot(data=filtered_results,\n",
+    "              cut=0,\n",
+    "              bw=0.2)\n",
+    "plt.title('Box Plot of Data Distributions')\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Value')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f2c50a8-098e-4230-b122-3880c8bb1883",
+   "metadata": {},
+   "source": [
+    "A violinplot gives a lot of the same information as a boxplot, in fact, there are little boxplots within the violinplot; the horizontal white line is the median, the thicker grey box is the IQR and the thin line shows the 1.5*IQR span. A violinplot also uses a kernel density extimator to visualize the distribution of each feature. Here we see that most of the data are clustered around relatively low values for all of the kron fluxes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ec470a9-8db0-403a-89ba-32e0dd9bef15",
+   "metadata": {},
+   "source": [
+    "## 5. Investigate the fluxes and associated flags"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18dba188-ea4c-47e9-8faf-62e3552add1e",
+   "metadata": {},
+   "source": [
+    "Use `pandas` to investigate if there are any flags on the `kronFlux` measurement. The `.value_counts()` method will show the number of True and False columns, where True are rows for which the `g_kronFlux` measurement was flagged for a variety of reasons. There are many other columns that investigate specific reasons why this measurement is untrustworthy; the `g_kronFlux_flag` is a way to combine all of the individual flags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0be4535d-cc89-45ef-98e9-591b9f459fae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:41.789910Z",
+     "iopub.status.busy": "2024-12-03T00:04:41.789726Z",
+     "iopub.status.idle": "2024-12-03T00:04:41.794781Z",
+     "shell.execute_reply": "2024-12-03T00:04:41.794289Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:41.789894Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "r_kronFlux_flag\n",
+       "False    10723\n",
+       "True       841\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['r_kronFlux_flag'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af623422-c6cf-4c21-8971-5599c60ea8d7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-15T18:24:36.531474Z",
+     "iopub.status.busy": "2024-11-15T18:24:36.531192Z",
+     "iopub.status.idle": "2024-11-15T18:24:36.547424Z",
+     "shell.execute_reply": "2024-11-15T18:24:36.546685Z",
+     "shell.execute_reply.started": "2024-11-15T18:24:36.531443Z"
+    }
+   },
+   "source": [
+    "Let's compare the value of the `g_kronFlux` between flagged and unflagged cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dddfd1e7-3faa-465f-ade4-dc136ae39262",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:41.795547Z",
+     "iopub.status.busy": "2024-12-03T00:04:41.795330Z",
+     "iopub.status.idle": "2024-12-03T00:04:41.924873Z",
+     "shell.execute_reply": "2024-12-03T00:04:41.924253Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:41.795530Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.clf()\n",
+    "plt.hist(results[results['r_kronFlux_flag']]['r_kronFlux'], label='Flagged', density=True)\n",
+    "#plt.hist(results[results['r_kronFlux_flag']==False]['r_kronFlux'], label='Not flagged', density=True)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e94c30-66b3-4b9c-b79b-bcf024d5f214",
+   "metadata": {},
+   "source": [
+    "Okay what about the `r_kronFlux` measurement?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0e66ccb2-3922-471b-8c15-7fb055d02a10",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:41.925819Z",
+     "iopub.status.busy": "2024-12-03T00:04:41.925606Z",
+     "iopub.status.idle": "2024-12-03T00:04:41.931416Z",
+     "shell.execute_reply": "2024-12-03T00:04:41.930843Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:41.925802Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "r_kronFlux_flag\n",
+       "False    10723\n",
+       "True       841\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['r_kronFlux_flag'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa7a4ce1-7bd4-47f8-a480-188b2c70579a",
+   "metadata": {},
+   "source": [
+    "Perform an intersection to see if the flagged entries overlap between these two photometric bands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "06786c33-2563-4237-9d0f-22d6308c0d7b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:41.932301Z",
+     "iopub.status.busy": "2024-12-03T00:04:41.932089Z",
+     "iopub.status.idle": "2024-12-03T00:04:41.970411Z",
+     "shell.execute_reply": "2024-12-03T00:04:41.969900Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:41.932283Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        coord_ra  coord_dec  g_kronFlux  g_kronFlux_flag  r_kronFlux  \\\n",
+      "1749   61.916451 -37.018987  232.455339             True  380.415747   \n",
+      "1758   61.910063 -37.017256  115.486047             True  105.318235   \n",
+      "1774   61.950787 -37.015252  132.207788             True  193.917364   \n",
+      "...          ...        ...         ...              ...         ...   \n",
+      "11466  61.956032 -37.074942   59.901381             True  315.077832   \n",
+      "11471  61.942023 -37.073313  145.759753             True  120.304211   \n",
+      "11494  61.924542 -37.071842  248.013148             True  273.729756   \n",
+      "\n",
+      "       r_kronFlux_flag  i_kronFlux  i_kronFlux_flag  \n",
+      "1749              True  562.754481            False  \n",
+      "1758              True  218.924537             True  \n",
+      "1774              True  522.751057            False  \n",
+      "...                ...         ...              ...  \n",
+      "11466             True         NaN             True  \n",
+      "11471             True  243.456290             True  \n",
+      "11494             True  257.108970             True  \n",
+      "\n",
+      "[328 rows x 8 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get the unique values (which will be True or False)\n",
+    "r_values = set(results['r_kronFlux_flag'].unique())\n",
+    "g_values = set(results['g_kronFlux_flag'].unique())\n",
+    "\n",
+    "# find the intersection\n",
+    "overlap = r_values & g_values\n",
+    "\n",
+    "overlap_true_rows = results[\n",
+    "    (results['r_kronFlux_flag'].isin(overlap)) & \n",
+    "    (results['g_kronFlux_flag'].isin(overlap)) & \n",
+    "    (results['r_kronFlux_flag'] == True) & \n",
+    "    (results['g_kronFlux_flag'] == True)\n",
+    "]\n",
+    "\n",
+    "print(overlap_true_rows)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec13b104-ad8d-4bd6-8a93-b6d1d57b921e",
+   "metadata": {},
+   "source": [
+    "There are six overlapping true rows, meaning that in six cases, we cannot use the $r-$band kronFlux to predict the $g-$band kronFlux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d29499cf-cab9-4a5c-8811-ffd1e5c9d82f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:41.971139Z",
+     "iopub.status.busy": "2024-12-03T00:04:41.970965Z",
+     "iopub.status.idle": "2024-12-03T00:04:41.978973Z",
+     "shell.execute_reply": "2024-12-03T00:04:41.978503Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:41.971124Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        coord_ra  coord_dec  g_kronFlux  g_kronFlux_flag  r_kronFlux  \\\n",
+      "46     62.036008 -36.904244   60.546756            False    0.103155   \n",
+      "347    62.072778 -36.964144   15.582472            False    0.000000   \n",
+      "700    62.067443 -36.954921   94.684525            False         NaN   \n",
+      "...          ...        ...         ...              ...         ...   \n",
+      "11477  61.984400 -37.069348   63.584932            False   85.069624   \n",
+      "11483  61.911589 -37.067708  159.077697            False  139.900920   \n",
+      "11493  61.957278 -37.071416  165.461776            False  182.058161   \n",
+      "\n",
+      "       r_kronFlux_flag  i_kronFlux  i_kronFlux_flag  \n",
+      "46                True   68.234239            False  \n",
+      "347               True  100.430859            False  \n",
+      "700               True  259.766837            False  \n",
+      "...                ...         ...              ...  \n",
+      "11477             True  100.221708             True  \n",
+      "11483             True  279.320760             True  \n",
+      "11493             True  349.606659             True  \n",
+      "\n",
+      "[513 rows x 8 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "g_false_r_true = results[\n",
+    "    (results['r_kronFlux_flag'] == True) & \n",
+    "    (results['g_kronFlux_flag'] == False)\n",
+    "]\n",
+    "\n",
+    "print(g_false_r_true)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69b67af5-de80-414c-985d-8f6cdccfc3a3",
+   "metadata": {},
+   "source": [
+    "For the unflagged values, let's look at the relationship between these fluxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e6294681-9c60-4ec6-805c-d378300acaa3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:41.979744Z",
+     "iopub.status.busy": "2024-12-03T00:04:41.979551Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.213955Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.213438Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:41.979728Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clean = results[\n",
+    "    (results['r_kronFlux_flag'] == False) & \n",
+    "    (results['g_kronFlux_flag'] == False) &\n",
+    "    (results['i_kronFlux_flag'] == False)\n",
+    "]\n",
+    "\n",
+    "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n",
+    "plt.xlabel(r'$g-$band kronFlux [nJy]')\n",
+    "plt.ylabel(r'$r-$band kronFlux [nJy]');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5afedb17-6478-4f2b-bdfc-38e73cd4a65e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.215968Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.215730Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.382817Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.382256Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.215949Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(clean['g_kronFlux'], clean['r_kronFlux'])\n",
+    "plt.xlabel(r'$g-$band kronFlux [nJy]')\n",
+    "plt.ylabel(r'$r-$band kronFlux [nJy]')\n",
+    "plt.xlim([0,30000])\n",
+    "plt.ylim([0,0.5e5]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f503394-3816-4d31-9cf0-9de88e229f87",
+   "metadata": {},
+   "source": [
+    "Zooming in on this relationship, it looks roughly linear, so we should be able to do some predictive work here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4704605a-4665-4ccc-bd7e-cefaf5e09828",
+   "metadata": {},
+   "source": [
+    "## 6. Prepare the training and test sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b3c8d4e-0541-49c7-9fa1-f9b3b5d29aac",
+   "metadata": {},
+   "source": [
+    "The first step is to define the training and validation data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ab2c517-125b-4c70-ad2d-b447f7e6721e",
+   "metadata": {},
+   "source": [
+    "The `.to_frame()` argument is required to input the X data as a 2D shape, as expected by scikit-learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fc90feca-ede1-44b0-929b-2fec1ddf5ad4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.383760Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.383543Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.389713Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.389149Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.383742Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    clean['g_kronFlux'].to_frame(), clean['r_kronFlux'].to_frame(), test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecb487df-ce82-4d2d-9821-64620e1e922b",
+   "metadata": {},
+   "source": [
+    "Good practice to use a scaler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8e675e27-74f0-43e8-91af-8652e2710609",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.390439Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.390274Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.404155Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.403690Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.390426Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4771e145-2649-4eda-8891-987c7e9c9009",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.404900Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.404711Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.413985Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.413340Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.404885Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.05664546],\n",
+       "       [ 0.0240961 ],\n",
+       "       [-0.06126054],\n",
+       "       ...,\n",
+       "       [ 0.51047933],\n",
+       "       [-0.01411049],\n",
+       "       [-0.03585177]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6011b2e6-5197-4475-8eb4-3a8841b3bd28",
+   "metadata": {},
+   "source": [
+    "## 7. Model (using `scikit-learn`)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48c8d648-288f-4635-a961-248f127fe524",
+   "metadata": {},
+   "source": [
+    "## 7.1 Start with a linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e02ca479-6105-442b-9879-2eb215dc4d66",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.414746Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.414553Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.430795Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.430269Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.414730Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "aca8064a-c94f-4792-86b3-62ec2130471b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.431529Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.431336Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.438736Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.438086Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.431514Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 81831676.75317723\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "print(\"MSE:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9086695-43c6-49e5-a751-bcabc275172d",
+   "metadata": {},
+   "source": [
+    "Plot how the predicted values compare to the true values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ee8bd887-928e-4a41-bd77-149b344ab238",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:42.439562Z",
+     "iopub.status.busy": "2024-12-03T00:04:42.439353Z",
+     "iopub.status.idle": "2024-12-03T00:04:42.606543Z",
+     "shell.execute_reply": "2024-12-03T00:04:42.605896Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:42.439546Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.clf()\n",
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.plot([0,1e6],[0,1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0,2e4])\n",
+    "plt.ylim([0,2e4]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1de26eb9-e7cf-4e94-bf14-0fda5d8efe18",
+   "metadata": {},
+   "source": [
+    "## 7.2 Improve the model with more features\n",
+    "Let's see if this will improve with more predictive values, this time including the i band information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d80e56eb-6e3d-4110-bee6-3681ee4a923b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:43.535589Z",
+     "iopub.status.busy": "2024-12-03T00:04:43.535315Z",
+     "iopub.status.idle": "2024-12-03T00:04:43.657195Z",
+     "shell.execute_reply": "2024-12-03T00:04:43.656652Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:43.535570Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 409962.71379404626\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    clean[['g_kronFlux', 'i_kronFlux']],  # Use these two features\n",
+    "    clean['r_kronFlux'],                 # Target variable\n",
+    "    test_size=0.2,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Standardize the features\n",
+    "scaler = StandardScaler()\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)\n",
+    "\n",
+    "# Train the model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "print(\"MSE:\", mse)\n",
+    "\n",
+    "# Scatter plot: True vs Predicted\n",
+    "plt.clf()\n",
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0, 2e4])\n",
+    "plt.ylim([0, 2e4])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30cd4c78-35e5-4459-84bf-9642068da002",
+   "metadata": {},
+   "source": [
+    "Test for the reader: try to improve this further by including more features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89e92033-ff41-4662-b5aa-ece41c216a07",
+   "metadata": {},
+   "source": [
+    "## 7.3 Random forest regressor\n",
+    "These are great"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "efb56f9d-6487-444d-b283-6d60c1694948",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:04:45.724451Z",
+     "iopub.status.busy": "2024-12-03T00:04:45.723900Z",
+     "iopub.status.idle": "2024-12-03T00:04:48.635415Z",
+     "shell.execute_reply": "2024-12-03T00:04:48.634885Z",
+     "shell.execute_reply.started": "2024-12-03T00:04:45.724427Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 33565886.06951628\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = RandomForestRegressor()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "print(\"MSE:\", mse)\n",
+    "\n",
+    "# Scatter plot: True vs Predicted\n",
+    "plt.clf()\n",
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0, 2e4])\n",
+    "plt.ylim([0, 2e4])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed0b4e6-904a-4af8-8bfe-cad402de09a4",
+   "metadata": {},
+   "source": [
+    "## 8. Hyperparameter tuning\n",
+    "With any `scikit-learn` model, it's possible to tune the hyperparameters to achieve better performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6420904-44bb-40cf-8ce0-92b22a802e59",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T17:29:45.803252Z",
+     "iopub.status.busy": "2024-12-03T17:29:45.802663Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "param_grid = {'n_estimators': [1, 10, 50, 100, 200, 1000, 10000]} # default 100 for n_estimators\n",
+    "\n",
+    "# Create GridSearchCV object\n",
+    "grid = GridSearchCV(model, param_grid, cv=5)\n",
+    "\n",
+    "grid.fit(X_train, y_train)\n",
+    "\n",
+    "# Get the best parameters\n",
+    "print(grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8445ad22-4275-4a56-ace7-ce1a678dd900",
+   "metadata": {},
+   "source": [
+    "### 8.2 Now retrieve the best fit model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "89167a5d-e835-4f8d-8d57-ac82f0df6239",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T00:19:21.191881Z",
+     "iopub.status.busy": "2024-12-03T00:19:21.191156Z",
+     "iopub.status.idle": "2024-12-03T00:19:21.944107Z",
+     "shell.execute_reply": "2024-12-03T00:19:21.943414Z",
+     "shell.execute_reply.started": "2024-12-03T00:19:21.191857Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Model: RandomForestRegressor(n_estimators=1000)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 19817809.291009396\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_model = grid.best_estimator_\n",
+    "print(\"Best Model:\", best_model)\n",
+    "y_pred = best_model.predict(X_test)\n",
+    "plt.clf()\n",
+    "plt.scatter(y_test, y_pred)\n",
+    "plt.plot([0, 1e6], [0, 1e6], color='black', ls='--')\n",
+    "plt.xlabel('True')\n",
+    "plt.ylabel('Predicted')\n",
+    "plt.xlim([0, 2e4])\n",
+    "plt.ylim([0, 2e4])\n",
+    "plt.show()\n",
+    "\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "print(\"MSE:\", mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5c33bd9-b95e-4ec8-a44d-cf3f219339e8",
+   "metadata": {},
+   "source": [
+    "## 9. Other available `scikit-learn` choices\n",
+    "The below two cells explore available options from `scikit-learn` for regression metrics and regression models, respectively. The metric cell is truncated with a `break` statement to only print details of the first metric. The model cell demonstrates printing the class information for the `RandomForestRegressor` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5adca7ed-eb36-4e8b-99e3-3b66833fb3e3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T17:56:30.644538Z",
+     "iopub.status.busy": "2024-12-03T17:56:30.643733Z",
+     "iopub.status.idle": "2024-12-03T17:56:30.650634Z",
+     "shell.execute_reply": "2024-12-03T17:56:30.649954Z",
+     "shell.execute_reply.started": "2024-12-03T17:56:30.644507Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['brier_score_loss', 'check_scoring', 'coverage_error', 'd2_absolute_error_score', 'd2_pinball_score', 'd2_tweedie_score', 'explained_variance_score', 'label_ranking_loss', 'log_loss', 'max_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_gamma_deviance', 'mean_pinball_loss', 'mean_poisson_deviance', 'mean_squared_error', 'mean_squared_log_error', 'mean_tweedie_deviance', 'median_absolute_error', 'pairwise_distances', 'r2_score', 'root_mean_squared_error', 'root_mean_squared_log_error']\n",
+      "--- mean_tweedie_deviance ---\n",
+      "Help on function mean_tweedie_deviance in module sklearn.metrics._regression:\n",
+      "\n",
+      "mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)\n",
+      "    Mean Tweedie deviance regression loss.\n",
+      "    \n",
+      "    Read more in the :ref:`User Guide <mean_tweedie_deviance>`.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    y_true : array-like of shape (n_samples,)\n",
+      "        Ground truth (correct) target values.\n",
+      "    \n",
+      "    y_pred : array-like of shape (n_samples,)\n",
+      "        Estimated target values.\n",
+      "    \n",
+      "    sample_weight : array-like of shape (n_samples,), default=None\n",
+      "        Sample weights.\n",
+      "    \n",
+      "    power : float, default=0\n",
+      "        Tweedie power parameter. Either power <= 0 or power >= 1.\n",
+      "    \n",
+      "        The higher `p` the less weight is given to extreme\n",
+      "        deviations between true and predicted targets.\n",
+      "    \n",
+      "        - power < 0: Extreme stable distribution. Requires: y_pred > 0.\n",
+      "        - power = 0 : Normal distribution, output corresponds to\n",
+      "          mean_squared_error. y_true and y_pred can be any real numbers.\n",
+      "        - power = 1 : Poisson distribution. Requires: y_true >= 0 and\n",
+      "          y_pred > 0.\n",
+      "        - 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0\n",
+      "          and y_pred > 0.\n",
+      "        - power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.\n",
+      "        - power = 3 : Inverse Gaussian distribution. Requires: y_true > 0\n",
+      "          and y_pred > 0.\n",
+      "        - otherwise : Positive stable distribution. Requires: y_true > 0\n",
+      "          and y_pred > 0.\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    loss : float\n",
+      "        A non-negative floating point value (the best value is 0.0).\n",
+      "    \n",
+      "    Examples\n",
+      "    --------\n",
+      "    >>> from sklearn.metrics import mean_tweedie_deviance\n",
+      "    >>> y_true = [2, 0, 1, 4]\n",
+      "    >>> y_pred = [0.5, 0.5, 2., 2.]\n",
+      "    >>> mean_tweedie_deviance(y_true, y_pred, power=1)\n",
+      "    1.4260...\n",
+      "\n",
+      "================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sklearn.metrics as metrics\n",
+    "import inspect\n",
+    "regression_metrics = [\n",
+    "    name for name, obj in inspect.getmembers(metrics)\n",
+    "    if inspect.isfunction(obj)\n",
+    "    and ('regression' in (obj.__doc__ or '').lower() or 'error' in (obj.__doc__ or '').lower())\n",
+    "    and 'classification' not in (obj.__doc__ or '').lower()\n",
+    "]\n",
+    "print(regression_metrics)\n",
+    "\n",
+    "\n",
+    "# Print the filtered metrics and their documentation\n",
+    "for metric in regression_metrics:\n",
+    "    metric_func = getattr(metrics, metric)\n",
+    "    if metric == \"mean_tweedie_deviance\":\n",
+    "        print(f\"--- {metric} ---\")\n",
+    "        help(metric_func)\n",
+    "        print(\"=\"*80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "de4a41f7-9f4f-4662-9302-2da7d68df434",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-12-03T17:40:53.931835Z",
+     "iopub.status.busy": "2024-12-03T17:40:53.931115Z",
+     "iopub.status.idle": "2024-12-03T17:40:54.007554Z",
+     "shell.execute_reply": "2024-12-03T17:40:54.007006Z",
+     "shell.execute_reply.started": "2024-12-03T17:40:53.931811Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ARDRegression\n",
+      "AdaBoostRegressor\n",
+      "BaggingRegressor\n",
+      "BayesianRidge\n",
+      "CCA\n",
+      "DecisionTreeRegressor\n",
+      "DummyRegressor\n",
+      "ElasticNet\n",
+      "ElasticNetCV\n",
+      "ExtraTreeRegressor\n",
+      "ExtraTreesRegressor\n",
+      "GammaRegressor\n",
+      "GaussianProcessRegressor\n",
+      "GradientBoostingRegressor\n",
+      "HistGradientBoostingRegressor\n",
+      "HuberRegressor\n",
+      "IsotonicRegression\n",
+      "KNeighborsRegressor\n",
+      "KernelRidge\n",
+      "Lars\n",
+      "LarsCV\n",
+      "Lasso\n",
+      "LassoCV\n",
+      "LassoLars\n",
+      "LassoLarsCV\n",
+      "LassoLarsIC\n",
+      "LinearRegression\n",
+      "LinearSVR\n",
+      "MLPRegressor\n",
+      "MultiOutputRegressor\n",
+      "MultiTaskElasticNet\n",
+      "MultiTaskElasticNetCV\n",
+      "MultiTaskLasso\n",
+      "MultiTaskLassoCV\n",
+      "NuSVR\n",
+      "OrthogonalMatchingPursuit\n",
+      "OrthogonalMatchingPursuitCV\n",
+      "PLSCanonical\n",
+      "PLSRegression\n",
+      "PassiveAggressiveRegressor\n",
+      "PoissonRegressor\n",
+      "QuantileRegressor\n",
+      "RANSACRegressor\n",
+      "RadiusNeighborsRegressor\n",
+      "RandomForestRegressor\n",
+      "Help on class RandomForestRegressor in module sklearn.ensemble._forest:\n",
+      "\n",
+      "class RandomForestRegressor(ForestRegressor)\n",
+      " |  RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n",
+      " |  \n",
+      " |  A random forest regressor.\n",
+      " |  \n",
+      " |  A random forest is a meta estimator that fits a number of decision tree\n",
+      " |  regressors on various sub-samples of the dataset and uses averaging to\n",
+      " |  improve the predictive accuracy and control over-fitting.\n",
+      " |  Trees in the forest use the best split strategy, i.e. equivalent to passing\n",
+      " |  `splitter=\"best\"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.\n",
+      " |  The sub-sample size is controlled with the `max_samples` parameter if\n",
+      " |  `bootstrap=True` (default), otherwise the whole dataset is used to build\n",
+      " |  each tree.\n",
+      " |  \n",
+      " |  For a comparison between tree-based ensemble models see the example\n",
+      " |  :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.\n",
+      " |  \n",
+      " |  Read more in the :ref:`User Guide <forest>`.\n",
+      " |  \n",
+      " |  Parameters\n",
+      " |  ----------\n",
+      " |  n_estimators : int, default=100\n",
+      " |      The number of trees in the forest.\n",
+      " |  \n",
+      " |      .. versionchanged:: 0.22\n",
+      " |         The default value of ``n_estimators`` changed from 10 to 100\n",
+      " |         in 0.22.\n",
+      " |  \n",
+      " |  criterion : {\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"},             default=\"squared_error\"\n",
+      " |      The function to measure the quality of a split. Supported criteria\n",
+      " |      are \"squared_error\" for the mean squared error, which is equal to\n",
+      " |      variance reduction as feature selection criterion and minimizes the L2\n",
+      " |      loss using the mean of each terminal node, \"friedman_mse\", which uses\n",
+      " |      mean squared error with Friedman's improvement score for potential\n",
+      " |      splits, \"absolute_error\" for the mean absolute error, which minimizes\n",
+      " |      the L1 loss using the median of each terminal node, and \"poisson\" which\n",
+      " |      uses reduction in Poisson deviance to find splits.\n",
+      " |      Training using \"absolute_error\" is significantly slower\n",
+      " |      than when using \"squared_error\".\n",
+      " |  \n",
+      " |      .. versionadded:: 0.18\n",
+      " |         Mean Absolute Error (MAE) criterion.\n",
+      " |  \n",
+      " |      .. versionadded:: 1.0\n",
+      " |         Poisson criterion.\n",
+      " |  \n",
+      " |  max_depth : int, default=None\n",
+      " |      The maximum depth of the tree. If None, then nodes are expanded until\n",
+      " |      all leaves are pure or until all leaves contain less than\n",
+      " |      min_samples_split samples.\n",
+      " |  \n",
+      " |  min_samples_split : int or float, default=2\n",
+      " |      The minimum number of samples required to split an internal node:\n",
+      " |  \n",
+      " |      - If int, then consider `min_samples_split` as the minimum number.\n",
+      " |      - If float, then `min_samples_split` is a fraction and\n",
+      " |        `ceil(min_samples_split * n_samples)` are the minimum\n",
+      " |        number of samples for each split.\n",
+      " |  \n",
+      " |      .. versionchanged:: 0.18\n",
+      " |         Added float values for fractions.\n",
+      " |  \n",
+      " |  min_samples_leaf : int or float, default=1\n",
+      " |      The minimum number of samples required to be at a leaf node.\n",
+      " |      A split point at any depth will only be considered if it leaves at\n",
+      " |      least ``min_samples_leaf`` training samples in each of the left and\n",
+      " |      right branches.  This may have the effect of smoothing the model,\n",
+      " |      especially in regression.\n",
+      " |  \n",
+      " |      - If int, then consider `min_samples_leaf` as the minimum number.\n",
+      " |      - If float, then `min_samples_leaf` is a fraction and\n",
+      " |        `ceil(min_samples_leaf * n_samples)` are the minimum\n",
+      " |        number of samples for each node.\n",
+      " |  \n",
+      " |      .. versionchanged:: 0.18\n",
+      " |         Added float values for fractions.\n",
+      " |  \n",
+      " |  min_weight_fraction_leaf : float, default=0.0\n",
+      " |      The minimum weighted fraction of the sum total of weights (of all\n",
+      " |      the input samples) required to be at a leaf node. Samples have\n",
+      " |      equal weight when sample_weight is not provided.\n",
+      " |  \n",
+      " |  max_features : {\"sqrt\", \"log2\", None}, int or float, default=1.0\n",
+      " |      The number of features to consider when looking for the best split:\n",
+      " |  \n",
+      " |      - If int, then consider `max_features` features at each split.\n",
+      " |      - If float, then `max_features` is a fraction and\n",
+      " |        `max(1, int(max_features * n_features_in_))` features are considered at each\n",
+      " |        split.\n",
+      " |      - If \"sqrt\", then `max_features=sqrt(n_features)`.\n",
+      " |      - If \"log2\", then `max_features=log2(n_features)`.\n",
+      " |      - If None or 1.0, then `max_features=n_features`.\n",
+      " |  \n",
+      " |      .. note::\n",
+      " |          The default of 1.0 is equivalent to bagged trees and more\n",
+      " |          randomness can be achieved by setting smaller values, e.g. 0.3.\n",
+      " |  \n",
+      " |      .. versionchanged:: 1.1\n",
+      " |          The default of `max_features` changed from `\"auto\"` to 1.0.\n",
+      " |  \n",
+      " |      Note: the search for a split does not stop until at least one\n",
+      " |      valid partition of the node samples is found, even if it requires to\n",
+      " |      effectively inspect more than ``max_features`` features.\n",
+      " |  \n",
+      " |  max_leaf_nodes : int, default=None\n",
+      " |      Grow trees with ``max_leaf_nodes`` in best-first fashion.\n",
+      " |      Best nodes are defined as relative reduction in impurity.\n",
+      " |      If None then unlimited number of leaf nodes.\n",
+      " |  \n",
+      " |  min_impurity_decrease : float, default=0.0\n",
+      " |      A node will be split if this split induces a decrease of the impurity\n",
+      " |      greater than or equal to this value.\n",
+      " |  \n",
+      " |      The weighted impurity decrease equation is the following::\n",
+      " |  \n",
+      " |          N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
+      " |                              - N_t_L / N_t * left_impurity)\n",
+      " |  \n",
+      " |      where ``N`` is the total number of samples, ``N_t`` is the number of\n",
+      " |      samples at the current node, ``N_t_L`` is the number of samples in the\n",
+      " |      left child, and ``N_t_R`` is the number of samples in the right child.\n",
+      " |  \n",
+      " |      ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
+      " |      if ``sample_weight`` is passed.\n",
+      " |  \n",
+      " |      .. versionadded:: 0.19\n",
+      " |  \n",
+      " |  bootstrap : bool, default=True\n",
+      " |      Whether bootstrap samples are used when building trees. If False, the\n",
+      " |      whole dataset is used to build each tree.\n",
+      " |  \n",
+      " |  oob_score : bool or callable, default=False\n",
+      " |      Whether to use out-of-bag samples to estimate the generalization score.\n",
+      " |      By default, :func:`~sklearn.metrics.r2_score` is used.\n",
+      " |      Provide a callable with signature `metric(y_true, y_pred)` to use a\n",
+      " |      custom metric. Only available if `bootstrap=True`.\n",
+      " |  \n",
+      " |  n_jobs : int, default=None\n",
+      " |      The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n",
+      " |      :meth:`decision_path` and :meth:`apply` are all parallelized over the\n",
+      " |      trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n",
+      " |      context. ``-1`` means using all processors. See :term:`Glossary\n",
+      " |      <n_jobs>` for more details.\n",
+      " |  \n",
+      " |  random_state : int, RandomState instance or None, default=None\n",
+      " |      Controls both the randomness of the bootstrapping of the samples used\n",
+      " |      when building trees (if ``bootstrap=True``) and the sampling of the\n",
+      " |      features to consider when looking for the best split at each node\n",
+      " |      (if ``max_features < n_features``).\n",
+      " |      See :term:`Glossary <random_state>` for details.\n",
+      " |  \n",
+      " |  verbose : int, default=0\n",
+      " |      Controls the verbosity when fitting and predicting.\n",
+      " |  \n",
+      " |  warm_start : bool, default=False\n",
+      " |      When set to ``True``, reuse the solution of the previous call to fit\n",
+      " |      and add more estimators to the ensemble, otherwise, just fit a whole\n",
+      " |      new forest. See :term:`Glossary <warm_start>` and\n",
+      " |      :ref:`tree_ensemble_warm_start` for details.\n",
+      " |  \n",
+      " |  ccp_alpha : non-negative float, default=0.0\n",
+      " |      Complexity parameter used for Minimal Cost-Complexity Pruning. The\n",
+      " |      subtree with the largest cost complexity that is smaller than\n",
+      " |      ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n",
+      " |      :ref:`minimal_cost_complexity_pruning` for details.\n",
+      " |  \n",
+      " |      .. versionadded:: 0.22\n",
+      " |  \n",
+      " |  max_samples : int or float, default=None\n",
+      " |      If bootstrap is True, the number of samples to draw from X\n",
+      " |      to train each base estimator.\n",
+      " |  \n",
+      " |      - If None (default), then draw `X.shape[0]` samples.\n",
+      " |      - If int, then draw `max_samples` samples.\n",
+      " |      - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,\n",
+      " |        `max_samples` should be in the interval `(0.0, 1.0]`.\n",
+      " |  \n",
+      " |      .. versionadded:: 0.22\n",
+      " |  \n",
+      " |  monotonic_cst : array-like of int of shape (n_features), default=None\n",
+      " |      Indicates the monotonicity constraint to enforce on each feature.\n",
+      " |        - 1: monotonically increasing\n",
+      " |        - 0: no constraint\n",
+      " |        - -1: monotonically decreasing\n",
+      " |  \n",
+      " |      If monotonic_cst is None, no constraints are applied.\n",
+      " |  \n",
+      " |      Monotonicity constraints are not supported for:\n",
+      " |        - multioutput regressions (i.e. when `n_outputs_ > 1`),\n",
+      " |        - regressions trained on data with missing values.\n",
+      " |  \n",
+      " |      Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.\n",
+      " |  \n",
+      " |      .. versionadded:: 1.4\n",
+      " |  \n",
+      " |  Attributes\n",
+      " |  ----------\n",
+      " |  estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor`\n",
+      " |      The child estimator template used to create the collection of fitted\n",
+      " |      sub-estimators.\n",
+      " |  \n",
+      " |      .. versionadded:: 1.2\n",
+      " |         `base_estimator_` was renamed to `estimator_`.\n",
+      " |  \n",
+      " |  estimators_ : list of DecisionTreeRegressor\n",
+      " |      The collection of fitted sub-estimators.\n",
+      " |  \n",
+      " |  feature_importances_ : ndarray of shape (n_features,)\n",
+      " |      The impurity-based feature importances.\n",
+      " |      The higher, the more important the feature.\n",
+      " |      The importance of a feature is computed as the (normalized)\n",
+      " |      total reduction of the criterion brought by that feature.  It is also\n",
+      " |      known as the Gini importance.\n",
+      " |  \n",
+      " |      Warning: impurity-based feature importances can be misleading for\n",
+      " |      high cardinality features (many unique values). See\n",
+      " |      :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
+      " |  \n",
+      " |  n_features_in_ : int\n",
+      " |      Number of features seen during :term:`fit`.\n",
+      " |  \n",
+      " |      .. versionadded:: 0.24\n",
+      " |  \n",
+      " |  feature_names_in_ : ndarray of shape (`n_features_in_`,)\n",
+      " |      Names of features seen during :term:`fit`. Defined only when `X`\n",
+      " |      has feature names that are all strings.\n",
+      " |  \n",
+      " |      .. versionadded:: 1.0\n",
+      " |  \n",
+      " |  n_outputs_ : int\n",
+      " |      The number of outputs when ``fit`` is performed.\n",
+      " |  \n",
+      " |  oob_score_ : float\n",
+      " |      Score of the training dataset obtained using an out-of-bag estimate.\n",
+      " |      This attribute exists only when ``oob_score`` is True.\n",
+      " |  \n",
+      " |  oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n",
+      " |      Prediction computed with out-of-bag estimate on the training set.\n",
+      " |      This attribute exists only when ``oob_score`` is True.\n",
+      " |  \n",
+      " |  estimators_samples_ : list of arrays\n",
+      " |      The subset of drawn samples (i.e., the in-bag samples) for each base\n",
+      " |      estimator. Each subset is defined by an array of the indices selected.\n",
+      " |  \n",
+      " |      .. versionadded:: 1.4\n",
+      " |  \n",
+      " |  See Also\n",
+      " |  --------\n",
+      " |  sklearn.tree.DecisionTreeRegressor : A decision tree regressor.\n",
+      " |  sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized\n",
+      " |      tree regressors.\n",
+      " |  sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient\n",
+      " |      Boosting Regression Tree, very fast for big datasets (n_samples >=\n",
+      " |      10_000).\n",
+      " |  \n",
+      " |  Notes\n",
+      " |  -----\n",
+      " |  The default values for the parameters controlling the size of the trees\n",
+      " |  (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n",
+      " |  unpruned trees which can potentially be very large on some data sets. To\n",
+      " |  reduce memory consumption, the complexity and size of the trees should be\n",
+      " |  controlled by setting those parameter values.\n",
+      " |  \n",
+      " |  The features are always randomly permuted at each split. Therefore,\n",
+      " |  the best found split may vary, even with the same training data,\n",
+      " |  ``max_features=n_features`` and ``bootstrap=False``, if the improvement\n",
+      " |  of the criterion is identical for several splits enumerated during the\n",
+      " |  search of the best split. To obtain a deterministic behaviour during\n",
+      " |  fitting, ``random_state`` has to be fixed.\n",
+      " |  \n",
+      " |  The default value ``max_features=1.0`` uses ``n_features``\n",
+      " |  rather than ``n_features / 3``. The latter was originally suggested in\n",
+      " |  [1], whereas the former was more recently justified empirically in [2].\n",
+      " |  \n",
+      " |  References\n",
+      " |  ----------\n",
+      " |  .. [1] L. Breiman, \"Random Forests\", Machine Learning, 45(1), 5-32, 2001.\n",
+      " |  \n",
+      " |  .. [2] P. Geurts, D. Ernst., and L. Wehenkel, \"Extremely randomized\n",
+      " |         trees\", Machine Learning, 63(1), 3-42, 2006.\n",
+      " |  \n",
+      " |  Examples\n",
+      " |  --------\n",
+      " |  >>> from sklearn.ensemble import RandomForestRegressor\n",
+      " |  >>> from sklearn.datasets import make_regression\n",
+      " |  >>> X, y = make_regression(n_features=4, n_informative=2,\n",
+      " |  ...                        random_state=0, shuffle=False)\n",
+      " |  >>> regr = RandomForestRegressor(max_depth=2, random_state=0)\n",
+      " |  >>> regr.fit(X, y)\n",
+      " |  RandomForestRegressor(...)\n",
+      " |  >>> print(regr.predict([[0, 0, 0, 0]]))\n",
+      " |  [-8.32987858]\n",
+      " |  \n",
+      " |  Method resolution order:\n",
+      " |      RandomForestRegressor\n",
+      " |      ForestRegressor\n",
+      " |      sklearn.base.RegressorMixin\n",
+      " |      BaseForest\n",
+      " |      sklearn.base.MultiOutputMixin\n",
+      " |      sklearn.ensemble._base.BaseEnsemble\n",
+      " |      sklearn.base.MetaEstimatorMixin\n",
+      " |      sklearn.base.BaseEstimator\n",
+      " |      sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin\n",
+      " |      sklearn.utils._metadata_requests._MetadataRequester\n",
+      " |      builtins.object\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __init__(self, n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)\n",
+      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      " |  \n",
+      " |  set_fit_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.<locals>\n",
+      " |      Request metadata passed to the ``fit`` method.\n",
+      " |      \n",
+      " |      Note that this method is only relevant if\n",
+      " |      ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n",
+      " |      Please see :ref:`User Guide <metadata_routing>` on how the routing\n",
+      " |      mechanism works.\n",
+      " |      \n",
+      " |      The options for each parameter are:\n",
+      " |      \n",
+      " |      - ``True``: metadata is requested, and passed to ``fit`` if provided. The request is ignored if metadata is not provided.\n",
+      " |      \n",
+      " |      - ``False``: metadata is not requested and the meta-estimator will not pass it to ``fit``.\n",
+      " |      \n",
+      " |      - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n",
+      " |      \n",
+      " |      - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n",
+      " |      \n",
+      " |      The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n",
+      " |      existing request. This allows you to change the request for some\n",
+      " |      parameters and not others.\n",
+      " |      \n",
+      " |      .. versionadded:: 1.3\n",
+      " |      \n",
+      " |      .. note::\n",
+      " |          This method is only relevant if this estimator is used as a\n",
+      " |          sub-estimator of a meta-estimator, e.g. used inside a\n",
+      " |          :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      sample_weight : str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED\n",
+      " |          Metadata routing for ``sample_weight`` parameter in ``fit``.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      self : object\n",
+      " |          The updated object.\n",
+      " |  \n",
+      " |  set_score_request(self: sklearn.ensemble._forest.RandomForestRegressor, *, sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> sklearn.ensemble._forest.RandomForestRegressor from sklearn.utils._metadata_requests.RequestMethod.__get__.<locals>\n",
+      " |      Request metadata passed to the ``score`` method.\n",
+      " |      \n",
+      " |      Note that this method is only relevant if\n",
+      " |      ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).\n",
+      " |      Please see :ref:`User Guide <metadata_routing>` on how the routing\n",
+      " |      mechanism works.\n",
+      " |      \n",
+      " |      The options for each parameter are:\n",
+      " |      \n",
+      " |      - ``True``: metadata is requested, and passed to ``score`` if provided. The request is ignored if metadata is not provided.\n",
+      " |      \n",
+      " |      - ``False``: metadata is not requested and the meta-estimator will not pass it to ``score``.\n",
+      " |      \n",
+      " |      - ``None``: metadata is not requested, and the meta-estimator will raise an error if the user provides it.\n",
+      " |      \n",
+      " |      - ``str``: metadata should be passed to the meta-estimator with this given alias instead of the original name.\n",
+      " |      \n",
+      " |      The default (``sklearn.utils.metadata_routing.UNCHANGED``) retains the\n",
+      " |      existing request. This allows you to change the request for some\n",
+      " |      parameters and not others.\n",
+      " |      \n",
+      " |      .. versionadded:: 1.3\n",
+      " |      \n",
+      " |      .. note::\n",
+      " |          This method is only relevant if this estimator is used as a\n",
+      " |          sub-estimator of a meta-estimator, e.g. used inside a\n",
+      " |          :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      sample_weight : str, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED\n",
+      " |          Metadata routing for ``sample_weight`` parameter in ``score``.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      self : object\n",
+      " |          The updated object.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  __abstractmethods__ = frozenset()\n",
+      " |  \n",
+      " |  __annotations__ = {'_parameter_constraints': <class 'dict'>}\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from ForestRegressor:\n",
+      " |  \n",
+      " |  predict(self, X)\n",
+      " |      Predict regression target for X.\n",
+      " |      \n",
+      " |      The predicted regression target of an input sample is computed as the\n",
+      " |      mean predicted regression targets of the trees in the forest.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
+      " |          The input samples. Internally, its dtype will be converted to\n",
+      " |          ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
+      " |          converted into a sparse ``csr_matrix``.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n",
+      " |          The predicted values.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from sklearn.base.RegressorMixin:\n",
+      " |  \n",
+      " |  score(self, X, y, sample_weight=None)\n",
+      " |      Return the coefficient of determination of the prediction.\n",
+      " |      \n",
+      " |      The coefficient of determination :math:`R^2` is defined as\n",
+      " |      :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n",
+      " |      sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n",
+      " |      is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n",
+      " |      The best possible score is 1.0 and it can be negative (because the\n",
+      " |      model can be arbitrarily worse). A constant model that always predicts\n",
+      " |      the expected value of `y`, disregarding the input features, would get\n",
+      " |      a :math:`R^2` score of 0.0.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      X : array-like of shape (n_samples, n_features)\n",
+      " |          Test samples. For some estimators this may be a precomputed\n",
+      " |          kernel matrix or a list of generic objects instead with shape\n",
+      " |          ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n",
+      " |          is the number of samples used in the fitting for the estimator.\n",
+      " |      \n",
+      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
+      " |          True values for `X`.\n",
+      " |      \n",
+      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
+      " |          Sample weights.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      score : float\n",
+      " |          :math:`R^2` of ``self.predict(X)`` w.r.t. `y`.\n",
+      " |      \n",
+      " |      Notes\n",
+      " |      -----\n",
+      " |      The :math:`R^2` score used when calling ``score`` on a regressor uses\n",
+      " |      ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n",
+      " |      with default value of :func:`~sklearn.metrics.r2_score`.\n",
+      " |      This influences the ``score`` method of all the multioutput\n",
+      " |      regressors (except for\n",
+      " |      :class:`~sklearn.multioutput.MultiOutputRegressor`).\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors inherited from sklearn.base.RegressorMixin:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from BaseForest:\n",
+      " |  \n",
+      " |  apply(self, X)\n",
+      " |      Apply trees in the forest to X, return leaf indices.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
+      " |          The input samples. Internally, its dtype will be converted to\n",
+      " |          ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
+      " |          converted into a sparse ``csr_matrix``.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      X_leaves : ndarray of shape (n_samples, n_estimators)\n",
+      " |          For each datapoint x in X and for each tree in the forest,\n",
+      " |          return the index of the leaf x ends up in.\n",
+      " |  \n",
+      " |  decision_path(self, X)\n",
+      " |      Return the decision path in the forest.\n",
+      " |      \n",
+      " |      .. versionadded:: 0.18\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
+      " |          The input samples. Internally, its dtype will be converted to\n",
+      " |          ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
+      " |          converted into a sparse ``csr_matrix``.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      indicator : sparse matrix of shape (n_samples, n_nodes)\n",
+      " |          Return a node indicator matrix where non zero elements indicates\n",
+      " |          that the samples goes through the nodes. The matrix is of CSR\n",
+      " |          format.\n",
+      " |      \n",
+      " |      n_nodes_ptr : ndarray of shape (n_estimators + 1,)\n",
+      " |          The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]\n",
+      " |          gives the indicator value for the i-th estimator.\n",
+      " |  \n",
+      " |  fit(self, X, y, sample_weight=None)\n",
+      " |      Build a forest of trees from the training set (X, y).\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
+      " |          The training input samples. Internally, its dtype will be converted\n",
+      " |          to ``dtype=np.float32``. If a sparse matrix is provided, it will be\n",
+      " |          converted into a sparse ``csc_matrix``.\n",
+      " |      \n",
+      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
+      " |          The target values (class labels in classification, real numbers in\n",
+      " |          regression).\n",
+      " |      \n",
+      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
+      " |          Sample weights. If None, then samples are equally weighted. Splits\n",
+      " |          that would create child nodes with net zero or negative weight are\n",
+      " |          ignored while searching for a split in each node. In the case of\n",
+      " |          classification, splits are also ignored if they would result in any\n",
+      " |          single class carrying a negative weight in either child node.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      self : object\n",
+      " |          Fitted estimator.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Readonly properties inherited from BaseForest:\n",
+      " |  \n",
+      " |  estimators_samples_\n",
+      " |      The subset of drawn samples for each base estimator.\n",
+      " |      \n",
+      " |      Returns a dynamically generated list of indices identifying\n",
+      " |      the samples used for fitting each member of the ensemble, i.e.,\n",
+      " |      the in-bag samples.\n",
+      " |      \n",
+      " |      Note: the list is re-created at each call to the property in order\n",
+      " |      to reduce the object memory footprint by not storing the sampling\n",
+      " |      data. Thus fetching the property may be slower than expected.\n",
+      " |  \n",
+      " |  feature_importances_\n",
+      " |      The impurity-based feature importances.\n",
+      " |      \n",
+      " |      The higher, the more important the feature.\n",
+      " |      The importance of a feature is computed as the (normalized)\n",
+      " |      total reduction of the criterion brought by that feature.  It is also\n",
+      " |      known as the Gini importance.\n",
+      " |      \n",
+      " |      Warning: impurity-based feature importances can be misleading for\n",
+      " |      high cardinality features (many unique values). See\n",
+      " |      :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      feature_importances_ : ndarray of shape (n_features,)\n",
+      " |          The values of this array sum to 1, unless all trees are single node\n",
+      " |          trees consisting of only the root node, in which case it will be an\n",
+      " |          array of zeros.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from sklearn.ensemble._base.BaseEnsemble:\n",
+      " |  \n",
+      " |  __getitem__(self, index)\n",
+      " |      Return the index'th estimator in the ensemble.\n",
+      " |  \n",
+      " |  __iter__(self)\n",
+      " |      Return iterator over estimators in the ensemble.\n",
+      " |  \n",
+      " |  __len__(self)\n",
+      " |      Return the number of estimators in the ensemble.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
+      " |  \n",
+      " |  __getstate__(self)\n",
+      " |      Helper for pickle.\n",
+      " |  \n",
+      " |  __repr__(self, N_CHAR_MAX=700)\n",
+      " |      Return repr(self).\n",
+      " |  \n",
+      " |  __setstate__(self, state)\n",
+      " |  \n",
+      " |  __sklearn_clone__(self)\n",
+      " |  \n",
+      " |  get_params(self, deep=True)\n",
+      " |      Get parameters for this estimator.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      deep : bool, default=True\n",
+      " |          If True, will return the parameters for this estimator and\n",
+      " |          contained subobjects that are estimators.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      params : dict\n",
+      " |          Parameter names mapped to their values.\n",
+      " |  \n",
+      " |  set_params(self, **params)\n",
+      " |      Set the parameters of this estimator.\n",
+      " |      \n",
+      " |      The method works on simple estimators as well as on nested objects\n",
+      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
+      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
+      " |      possible to update each component of a nested object.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      **params : dict\n",
+      " |          Estimator parameters.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      self : estimator instance\n",
+      " |          Estimator instance.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n",
+      " |  \n",
+      " |  get_metadata_routing(self)\n",
+      " |      Get metadata routing of this object.\n",
+      " |      \n",
+      " |      Please check :ref:`User Guide <metadata_routing>` on how the routing\n",
+      " |      mechanism works.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      routing : MetadataRequest\n",
+      " |          A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating\n",
+      " |          routing information.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Class methods inherited from sklearn.utils._metadata_requests._MetadataRequester:\n",
+      " |  \n",
+      " |  __init_subclass__(**kwargs)\n",
+      " |      Set the ``set_{method}_request`` methods.\n",
+      " |      \n",
+      " |      This uses PEP-487 [1]_ to set the ``set_{method}_request`` methods. It\n",
+      " |      looks for the information available in the set default values which are\n",
+      " |      set using ``__metadata_request__*`` class attributes, or inferred\n",
+      " |      from method signatures.\n",
+      " |      \n",
+      " |      The ``__metadata_request__*`` class attributes are used when a method\n",
+      " |      does not explicitly accept a metadata through its arguments or if the\n",
+      " |      developer would like to specify a request value for those metadata\n",
+      " |      which are different from the default ``None``.\n",
+      " |      \n",
+      " |      References\n",
+      " |      ----------\n",
+      " |      .. [1] https://www.python.org/dev/peps/pep-0487\n",
+      "\n",
+      "None\n",
+      "RegressorChain\n",
+      "Ridge\n",
+      "RidgeCV\n",
+      "SGDRegressor\n",
+      "SVR\n",
+      "StackingRegressor\n",
+      "TheilSenRegressor\n",
+      "TransformedTargetRegressor\n",
+      "TweedieRegressor\n",
+      "VotingRegressor\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.utils import all_estimators\n",
+    "\n",
+    "# Get all regression models\n",
+    "regressors = all_estimators(type_filter='regressor')\n",
+    "\n",
+    "# Print the names of all available regression models\n",
+    "for name, estimator in regressors:\n",
+    "    print(name)\n",
+    "\n",
+    "for name, estimator in regressors:\n",
+    "    if name == \"RandomForestRegressor\":\n",
+    "        print(help(estimator))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be44f0da-e766-4bd0-9312-4b1977397c8d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "LSST",
+   "language": "python",
+   "name": "lsst"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}