diff --git a/doc/tutorials/tutorial_edcs_churn.ipynb b/doc/tutorials/tutorial_edcs_churn.ipynb
new file mode 100644
index 0000000..57c20ae
--- /dev/null
+++ b/doc/tutorials/tutorial_edcs_churn.ipynb
@@ -0,0 +1,2914 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# LECTURA DE LA BASE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pandas import DataFrame \n",
+    "from sklearn.tree import DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "with zipfile.ZipFile('cost_sensitive_classification_churn.csv.zip', 'r') as z:\n",
+    "    f = z.open('cost_sensitive_classification_churn.csv')\n",
+    "    data = pd.io.parsers.read_table(f, sep=',')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Target, variable que reporta el abandono o no de los clientes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>x42</th>\n",
+       "      <th>x43</th>\n",
+       "      <th>x44</th>\n",
+       "      <th>x45</th>\n",
+       "      <th>x46</th>\n",
+       "      <th>C_FP</th>\n",
+       "      <th>C_FN</th>\n",
+       "      <th>C_TP</th>\n",
+       "      <th>C_TN</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1028.571429</td>\n",
+       "      <td>121.828571</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>53.428571</td>\n",
+       "      <td>1028.571429</td>\n",
+       "      <td>82.742857</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>66.285714</td>\n",
+       "      <td>1285.714286</td>\n",
+       "      <td>102.928571</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>92.000000</td>\n",
+       "      <td>1285.714286</td>\n",
+       "      <td>151.785714</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>53.428571</td>\n",
+       "      <td>1028.571429</td>\n",
+       "      <td>82.742857</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  x1  x2  x3  x4  x5  x6  x7  x8  x9   ...    x42  x43  x44  x45  x46  \\\n",
+       "0   0   0   1   1   1   0   1   1   0   0   ...      1    1    5    2    2   \n",
+       "1   1   0   1   1   1   0   1   1   0   0   ...      3    1    5    2    4   \n",
+       "2   2   1   1   1   1   0   1   1   0   0   ...      1    8    3    1    4   \n",
+       "3   3   1   1   1   0   0   1   1   0   0   ...      1    8    4    3    2   \n",
+       "4   4   0   1   1   1   0   1   1   0   0   ...      1    7    5    2    4   \n",
+       "\n",
+       "        C_FP         C_FN        C_TP  C_TN  target  \n",
+       "0  74.000000  1028.571429  121.828571     0       0  \n",
+       "1  53.428571  1028.571429   82.742857     0       0  \n",
+       "2  66.285714  1285.714286  102.928571     0       0  \n",
+       "3  92.000000  1285.714286  151.785714     0       0  \n",
+       "4  53.428571  1028.571429   82.742857     0       0  \n",
+       "\n",
+       "[5 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9379, 52)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Propoción de la varible target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    8930\n",
+       "1     449\n",
+       "Name: target, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.target.value_counts(normalize=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.952127\n",
+       "1    0.047873\n",
+       "Name: target, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.target.value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X =data[['x'+str(i) for i in range(1, 47)]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "      <th>x10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>x37</th>\n",
+       "      <th>x38</th>\n",
+       "      <th>x39</th>\n",
+       "      <th>x40</th>\n",
+       "      <th>x41</th>\n",
+       "      <th>x42</th>\n",
+       "      <th>x43</th>\n",
+       "      <th>x44</th>\n",
+       "      <th>x45</th>\n",
+       "      <th>x46</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.208086</td>\n",
+       "      <td>-0.066014</td>\n",
+       "      <td>-0.075314</td>\n",
+       "      <td>-0.435398</td>\n",
+       "      <td>-0.04151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.208086</td>\n",
+       "      <td>-0.066014</td>\n",
+       "      <td>-0.075314</td>\n",
+       "      <td>-0.435398</td>\n",
+       "      <td>-0.04151</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.208086</td>\n",
+       "      <td>-0.066014</td>\n",
+       "      <td>-0.075314</td>\n",
+       "      <td>0.836751</td>\n",
+       "      <td>-0.04151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.208086</td>\n",
+       "      <td>-0.066014</td>\n",
+       "      <td>-0.075314</td>\n",
+       "      <td>0.836751</td>\n",
+       "      <td>-0.04151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.208086</td>\n",
+       "      <td>-0.066014</td>\n",
+       "      <td>-0.075314</td>\n",
+       "      <td>-0.435398</td>\n",
+       "      <td>-0.04151</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x1  x2  x3  x4  x5  x6  x7  x8  x9  x10 ...        x37       x38       x39  \\\n",
+       "0   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014 -0.075314   \n",
+       "1   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014 -0.075314   \n",
+       "2   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014 -0.075314   \n",
+       "3   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014 -0.075314   \n",
+       "4   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014 -0.075314   \n",
+       "\n",
+       "        x40      x41  x42  x43  x44  x45  x46  \n",
+       "0 -0.435398 -0.04151    1    1    5    2    2  \n",
+       "1 -0.435398 -0.04151    3    1    5    2    4  \n",
+       "2  0.836751 -0.04151    1    8    3    1    4  \n",
+       "3  0.836751 -0.04151    1    8    4    3    2  \n",
+       "4 -0.435398 -0.04151    1    7    5    2    4  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y = data.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    0\n",
+       "2    0\n",
+       "3    0\n",
+       "4    0\n",
+       "Name: target, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    8930\n",
+       "1     449\n",
+       "Name: target, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.value_counts(normalize=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matriz de costos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "cost_mat = data[['C_FP','C_FN','C_TP','C_TN']].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   74.      ,  1028.571429,   121.828571,     0.      ],\n",
+       "       [   53.428571,  1028.571429,    82.742857,     0.      ],\n",
+       "       [   66.285714,  1285.714286,   102.928571,     0.      ],\n",
+       "       ..., \n",
+       "       [   98.      ,  1371.428571,   161.771429,     0.      ],\n",
+       "       [   53.428571,  1028.571429,    82.742857,     0.      ],\n",
+       "       [   53.428571,  1028.571429,    82.742857,     0.      ]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Training and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from  sklearn.cross_validation import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "temp = train_test_split(X, y, cost_mat, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test, cost_mat_train, cost_mat_test = temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[      x1  x2  x3  x4  x5  x6  x7  x8  x9  x10 ...        x37       x38  \\\n",
+       " 5667   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7263   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1563   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4950   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8030   0   1   1   1   1   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7040   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1979   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1883   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 438    0   1   1   1   0   0   1   1   0    1 ...   3.071972 -0.066014   \n",
+       " 9049   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3794   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8849   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4113   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2986   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6828   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8640   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2280   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8756   0   1   1   1   0   0   1   1   0    1 ...   6.352029 -0.066014   \n",
+       " 7536   0   1   1   0   1   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4712   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 373    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4539   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8644   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2585   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1332   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7838   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 9047   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3774   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1784   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 9374   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " ...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ... ...        ...       ...   \n",
+       " 2734   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 189    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 9167   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2747   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2047   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7849   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2558   1   1   1   0   1   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 9274   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8666   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6396   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3385   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4555   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1184   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6420   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5051   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5311   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2433   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6949   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 769    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1685   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8322   0   1   1   1   0   0   1   0   0    1 ...   1.431943 -0.066014   \n",
+       " 5578   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4426   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 466    1   1   1   0   0   1   0   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6265   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5734   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5191   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5390   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 860    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7270   0   1   1   1   1   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " \n",
+       "            x39       x40      x41  x42  x43  x44  x45  x46  \n",
+       " 5667 -0.075314 -0.435398 -0.04151    2    8    2    1    4  \n",
+       " 7263 -0.075314  3.381048 -0.04151    1    8    1    2    3  \n",
+       " 1563 -0.075314 -0.435398 -0.04151    1    1    1    2    3  \n",
+       " 4950 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 8030 -0.075314 -0.435398 -0.04151    3    1    3    2    2  \n",
+       " 7040 -0.075314 -0.435398 -0.04151    1    8    3    1    2  \n",
+       " 1979 -0.075314 -0.435398 -0.04151    2    8    5    2    4  \n",
+       " 1883 -0.075314 -0.435398 -0.04151    2    7    3    1    2  \n",
+       " 438  -0.075314 -0.435398 -0.04151    2    8    5    2    4  \n",
+       " 9049 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 3794 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 8849 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 4113 -0.075314  3.381048 -0.04151    1    8    3    2    2  \n",
+       " 2986 -0.075314  0.836751 -0.04151    2    8    3    2    4  \n",
+       " 6828 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 8640 -0.075314  0.836751 -0.04151    1    2    4    2    1  \n",
+       " 2280 -0.075314 -0.435398 -0.04151    2    7    5    2    2  \n",
+       " 8756 -0.075314 -0.435398 -0.04151    1    7    1    2    4  \n",
+       " 7536 -0.075314 -0.435398 -0.04151    2    7    5    2    2  \n",
+       " 4712 -0.075314 -0.435398 -0.04151    1    1    5    2    2  \n",
+       " 373  -0.075314 -0.435398 -0.04151    1    8    4    2    2  \n",
+       " 4539 -0.075314  3.381048 -0.04151    2    8    1    3    2  \n",
+       " 8644 -0.075314 -0.435398 -0.04151    3    1    3    1    2  \n",
+       " 2585 -0.075314 -0.435398 -0.04151    1    8    5    1    4  \n",
+       " 1332 -0.075314 -0.435398 -0.04151    2    8    3    1    4  \n",
+       " 7838 -0.075314 -0.435398 -0.04151    1    7    3    2    4  \n",
+       " 9047 -0.075314 -0.435398 -0.04151    2    7    5    1    4  \n",
+       " 3774 -0.075314  0.836751 -0.04151    2    8    1    3    2  \n",
+       " 1784 -0.075314 -0.435398 -0.04151    1    8    3    2    2  \n",
+       " 9374 -0.075314 -0.435398 -0.04151    2    7    3    1    4  \n",
+       " ...        ...       ...      ...  ...  ...  ...  ...  ...  \n",
+       " 2734 -0.075314  0.836751 -0.04151    3    7    3    1    2  \n",
+       " 189  -0.075314 -0.435398 -0.04151    1    7    4    2    4  \n",
+       " 9167 -0.075314 -0.435398 -0.04151    1    8    5    3    4  \n",
+       " 2747 -0.075314 -0.435398 -0.04151    1    8    4    2    4  \n",
+       " 2047 -0.075314 -0.435398 -0.04151    2    8    3    2    2  \n",
+       " 7849 -0.075314 -0.435398 -0.04151    2    7    5    2    4  \n",
+       " 2558 -0.075314  0.836751 -0.04151    1    8    4    2    2  \n",
+       " 9274 -0.075314 -0.435398 -0.04151    3    7    5    2    2  \n",
+       " 8666 -0.075314 -0.435398 -0.04151    2    3    4    2    2  \n",
+       " 6396 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 3385 -0.075314  0.836751 -0.04151    1    8    5    2    2  \n",
+       " 4555 -0.075314 -0.435398 -0.04151    3    8    5    2    4  \n",
+       " 1184 -0.075314 -0.435398 -0.04151    1   12    4    2    2  \n",
+       " 6420 -0.075314 -0.435398 -0.04151    3    7    5    3    4  \n",
+       " 5051 -0.075314 -0.435398 -0.04151    1    8    4    3    2  \n",
+       " 5311 -0.075314  0.836751 -0.04151    1    7    3    1    2  \n",
+       " 2433 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 6949 -0.075314  0.836751 -0.04151    1    8    3    1    4  \n",
+       " 769  -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 1685 -0.075314 -0.435398 -0.04151    1    1    5    2    3  \n",
+       " 8322 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 5578 -0.075314 -0.435398 -0.04151    1    1    5    2    2  \n",
+       " 4426 -0.075314 -0.435398 -0.04151    1    8    1    2    3  \n",
+       " 466  -0.075314  0.836751 -0.04151    1    2    4    2    3  \n",
+       " 6265 -0.075314  0.836751 -0.04151    1    8    3    1    4  \n",
+       " 5734 -0.075314 -0.435398 -0.04151    2    7    5    2    4  \n",
+       " 5191 -0.075314 -0.435398 -0.04151    1    7    5    2    2  \n",
+       " 5390 -0.075314 -0.435398 -0.04151    2    8    5    2    3  \n",
+       " 860  -0.075314 -0.435398 -0.04151    1    8    3    2    4  \n",
+       " 7270 -0.075314 -0.435398 -0.04151    1    8    3    2    4  \n",
+       " \n",
+       " [6283 rows x 46 columns],\n",
+       "       x1  x2  x3  x4  x5  x6  x7  x8  x9  x10 ...        x37       x38  \\\n",
+       " 2260   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4428   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5304   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2246   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6069   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8435   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 360    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2802   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7690   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7143   0   1   1   1   0   0   1   0   0    1 ...   1.431943 -0.066014   \n",
+       " 169    0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 9188   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4344   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3208   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 483    0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3261   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7766   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1183   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3307   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7931   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3473   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1603   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2287   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3838   0   0   1   1   0   0   1   0   0    1 ...   1.431943 -0.066014   \n",
+       " 1084   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5418   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8159   1   0   1   0   0   0   1   0   0    1 ...   4.712000 -0.066014   \n",
+       " 8410   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 416    0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3653   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " ...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ... ...        ...       ...   \n",
+       " 1669   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2112   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8035   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6066   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7015   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5404   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 84     0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6086   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7229   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2283   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2887   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8131   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 5281   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 418    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7888   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 7367   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 2709   0   1   1   1   0   0   1   1   0    1 ...   7.992058 -0.066014   \n",
+       " 2357   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 6578   1   1   1   0   0   1   0   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1366   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 293    0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 9325   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8869   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1506   1   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8468   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8405   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 1634   0   1   1   1   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 4011   1   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 8290   0   1   1   0   0   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " 3315   1   1   1   0   1   1   1   0   0    1 ...  -0.208086 -0.066014   \n",
+       " \n",
+       "            x39       x40      x41  x42  x43  x44  x45  x46  \n",
+       " 2260 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 4428 -0.075314  0.836751 -0.04151    1    8    3    2    4  \n",
+       " 5304 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 2246 -0.075314 -0.435398 -0.04151    1    8    1    2    4  \n",
+       " 6069 -0.075314 -0.435398 -0.04151    2    8    4    2    2  \n",
+       " 8435 -0.075314  0.836751 -0.04151    1    8    4    1    4  \n",
+       " 360  -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 2802 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 7690 -0.075314 -0.435398 -0.04151    1    7    5    1    3  \n",
+       " 7143 -0.075314 -0.435398 -0.04151    2    8    5    2    4  \n",
+       " 169  -0.075314 -0.435398 -0.04151    3    7    5    2    4  \n",
+       " 9188 -0.075314 -0.435398 -0.04151    1    8    5    1    4  \n",
+       " 4344 -0.075314 -0.435398 -0.04151    2    8    5    1    4  \n",
+       " 3208 -0.075314  0.836751 -0.04151    2    7    3    2    4  \n",
+       " 483  -0.075314 -0.435398 -0.04151    2    7    2    1    4  \n",
+       " 3261 -0.075314 -0.435398 -0.04151    3    7    5    2    4  \n",
+       " 7766 -0.075314 -0.435398 -0.04151    2    8    3    2    4  \n",
+       " 1183 -0.075314 -0.435398 -0.04151    1    7    2    1    4  \n",
+       " 3307 -0.075314 -0.435398 -0.04151    1    1    5    2    2  \n",
+       " 7931 -0.075314 -0.435398 -0.04151    2    7    5    2    4  \n",
+       " 3473 -0.075314  0.836751 -0.04151    1    8    4    2    2  \n",
+       " 1603 -0.075314 -0.435398 -0.04151    1    7    3    1    4  \n",
+       " 2287 -0.075314 -0.435398 -0.04151    3    1    5    3    4  \n",
+       " 3838 -0.075314 -0.435398 -0.04151    1    8    2    2    4  \n",
+       " 1084 -0.075314 -0.435398 -0.04151    2    7    5    2    4  \n",
+       " 5418 -0.075314 -0.435398 -0.04151    2    8    3    2    4  \n",
+       " 8159 -0.075314  3.381048 -0.04151    1    8    5    2    4  \n",
+       " 8410 -0.075314 -0.435398 -0.04151    1    8    3    2    4  \n",
+       " 416  -0.075314 -0.435398 -0.04151    2    6    2    2    4  \n",
+       " 3653 -0.075314 -0.435398 -0.04151    1    8    4    2    4  \n",
+       " ...        ...       ...      ...  ...  ...  ...  ...  ...  \n",
+       " 1669 -0.075314 -0.435398 -0.04151    1    1    5    2    2  \n",
+       " 2112 -0.075314  0.836751 -0.04151    1    2    4    3    3  \n",
+       " 8035 -0.075314 -0.435398 -0.04151    1    1    5    2    2  \n",
+       " 6066 -0.075314 -0.435398 -0.04151    3    8    4    2    4  \n",
+       " 7015 -0.075314 -0.435398 -0.04151    2    8    1    2    4  \n",
+       " 5404 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 84   -0.075314 -0.435398 -0.04151    2    8    5    2    4  \n",
+       " 6086 -0.075314 -0.435398 -0.04151    2    1    1    2    3  \n",
+       " 7229 -0.075314 -0.435398 -0.04151    2    1    5    2    3  \n",
+       " 2283 -0.075314  0.836751 -0.04151    1    8    5    2    2  \n",
+       " 2887 -0.075314 -0.435398 -0.04151    2    8    3    2    4  \n",
+       " 8131 -0.075314 -0.435398 -0.04151    3    8    1    3    4  \n",
+       " 5281 -0.075314 -0.435398 -0.04151    2    7    5    2    4  \n",
+       " 418  -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 7888 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 7367 -0.075314 -0.435398 -0.04151    2    1    5    2    2  \n",
+       " 2709 -0.075314 -0.435398 -0.04151    3    8    3    2    4  \n",
+       " 2357 -0.075314 -0.435398 -0.04151    3    7    3    2    2  \n",
+       " 6578 -0.075314 -0.435398 -0.04151    1    8    3    2    4  \n",
+       " 1366 -0.075314 -0.435398 -0.04151    1    8    5    2    4  \n",
+       " 293  -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 9325 -0.075314  0.836751 -0.04151    1    8    5    2    4  \n",
+       " 8869 -0.075314 -0.435398 -0.04151    1    7    3    2    2  \n",
+       " 1506 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " 8468 -0.075314  3.381048 -0.04151    1    8    3    2    2  \n",
+       " 8405 -0.075314 -0.435398 -0.04151    3    7    3    2    2  \n",
+       " 1634 -0.075314 -0.435398 -0.04151    1    1    3    2    4  \n",
+       " 4011 -0.075314  3.381048 -0.04151    3    8    5    1    4  \n",
+       " 8290 -0.075314 -0.435398 -0.04151    1    8    1    2    2  \n",
+       " 3315 -0.075314 -0.435398 -0.04151    1    7    5    2    4  \n",
+       " \n",
+       " [3096 rows x 46 columns],\n",
+       " 5667    0\n",
+       " 7263    0\n",
+       " 1563    0\n",
+       " 4950    0\n",
+       " 8030    0\n",
+       " 7040    0\n",
+       " 1979    0\n",
+       " 1883    0\n",
+       " 438     0\n",
+       " 9049    0\n",
+       " 3794    0\n",
+       " 8849    0\n",
+       " 4113    0\n",
+       " 2986    0\n",
+       " 6828    0\n",
+       " 8640    0\n",
+       " 2280    0\n",
+       " 8756    0\n",
+       " 7536    0\n",
+       " 4712    0\n",
+       " 373     0\n",
+       " 4539    0\n",
+       " 8644    0\n",
+       " 2585    0\n",
+       " 1332    0\n",
+       " 7838    0\n",
+       " 9047    0\n",
+       " 3774    0\n",
+       " 1784    0\n",
+       " 9374    0\n",
+       "        ..\n",
+       " 2734    0\n",
+       " 189     0\n",
+       " 9167    0\n",
+       " 2747    0\n",
+       " 2047    1\n",
+       " 7849    0\n",
+       " 2558    0\n",
+       " 9274    0\n",
+       " 8666    0\n",
+       " 6396    0\n",
+       " 3385    0\n",
+       " 4555    0\n",
+       " 1184    0\n",
+       " 6420    0\n",
+       " 5051    0\n",
+       " 5311    0\n",
+       " 2433    0\n",
+       " 6949    0\n",
+       " 769     0\n",
+       " 1685    0\n",
+       " 8322    0\n",
+       " 5578    0\n",
+       " 4426    0\n",
+       " 466     0\n",
+       " 6265    0\n",
+       " 5734    0\n",
+       " 5191    0\n",
+       " 5390    0\n",
+       " 860     0\n",
+       " 7270    0\n",
+       " Name: target, dtype: float64,\n",
+       " 2260    0\n",
+       " 4428    0\n",
+       " 5304    0\n",
+       " 2246    0\n",
+       " 6069    0\n",
+       " 8435    0\n",
+       " 360     0\n",
+       " 2802    0\n",
+       " 7690    0\n",
+       " 7143    0\n",
+       " 169     1\n",
+       " 9188    0\n",
+       " 4344    0\n",
+       " 3208    0\n",
+       " 483     0\n",
+       " 3261    0\n",
+       " 7766    0\n",
+       " 1183    0\n",
+       " 3307    0\n",
+       " 7931    0\n",
+       " 3473    0\n",
+       " 1603    0\n",
+       " 2287    0\n",
+       " 3838    0\n",
+       " 1084    0\n",
+       " 5418    0\n",
+       " 8159    0\n",
+       " 8410    0\n",
+       " 416     0\n",
+       " 3653    0\n",
+       "        ..\n",
+       " 1669    1\n",
+       " 2112    0\n",
+       " 8035    0\n",
+       " 6066    0\n",
+       " 7015    0\n",
+       " 5404    0\n",
+       " 84      0\n",
+       " 6086    0\n",
+       " 7229    0\n",
+       " 2283    0\n",
+       " 2887    0\n",
+       " 8131    0\n",
+       " 5281    0\n",
+       " 418     0\n",
+       " 7888    0\n",
+       " 7367    0\n",
+       " 2709    0\n",
+       " 2357    0\n",
+       " 6578    0\n",
+       " 1366    0\n",
+       " 293     0\n",
+       " 9325    0\n",
+       " 8869    0\n",
+       " 1506    1\n",
+       " 8468    0\n",
+       " 8405    0\n",
+       " 1634    0\n",
+       " 4011    0\n",
+       " 8290    0\n",
+       " 3315    0\n",
+       " Name: target, dtype: float64,\n",
+       " array([[   53.428571,  1028.571429,    82.742857,     0.      ],\n",
+       "        [  171.714286,  1885.714286,   308.994286,     0.      ],\n",
+       "        [  148.571429,  1628.571429,   267.131429,     0.      ],\n",
+       "        ..., \n",
+       "        [  110.      ,  1200.      ,   197.36    ,     0.      ],\n",
+       "        [   87.714286,  1714.285714,   136.571429,     0.      ],\n",
+       "        [   62.      ,  1200.      ,    96.2     ,     0.      ]]),\n",
+       " array([[   62.      ,  1200.      ,    96.2     ,     0.      ],\n",
+       "        [   87.714286,  1714.285714,   136.571429,     0.      ],\n",
+       "        [   53.428571,  1028.571429,    82.742857,     0.      ],\n",
+       "        ..., \n",
+       "        [   66.285714,  1285.714286,   102.928571,     0.      ],\n",
+       "        [   74.      ,  1028.571429,   121.828571,     0.      ],\n",
+       "        [   66.285714,  1285.714286,   102.928571,     0.      ]])]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2939\n",
+       "1     157\n",
+       "Name: target, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test.value_counts(normalize=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    5991\n",
+       "1     292\n",
+       "Name: target, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.value_counts(normalize=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Bagging y Minimum risk\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import BaggingClassifier\n",
+    "cost_loss_tot=0\n",
+    "\n",
+    "res =np.zeros(100)\n",
+    "res1=np.zeros(100)\n",
+    "res2=np.zeros(100)\n",
+    "\n",
+    "for w in range (1,101):\n",
+    "      \n",
+    "    \n",
+    "\n",
+    "    dt = DecisionTreeClassifier(max_depth=4,random_state=42)\n",
+    "    bagreg = BaggingClassifier(dt, n_estimators=w, \n",
+    "                              bootstrap=True, oob_score=True, random_state=1)\n",
+    "\n",
+    "    bagreg.fit(X_train, y_train)\n",
+    "    \n",
+    "    y_pred = bagreg.predict_proba(X_test)\n",
+    "\n",
+    "    # 'C_FP','C_FN','C_TP','C_TN'\n",
+    "\n",
+    "    y_pred_metacost = np.zeros(X_test.shape[0])\n",
+    "\n",
+    "\n",
+    "    for i in range(X_test.shape[0]):\n",
+    "        c_j0 = y_pred[i, 0] * cost_mat_test[i, 3] + y_pred[i, 1]* cost_mat_test[i, 1]\n",
+    "        c_j1 = y_pred[i, 0] * cost_mat_test[i, 0] + y_pred[i, 1]* cost_mat_test[i, 2]\n",
+    "    \n",
+    "        if c_j0 > c_j1:\n",
+    "            y_pred_metacost[i] = 1\n",
+    "\n",
+    "    y_pred_bagging = bagreg.predict(X_test)\n",
+    "\n",
+    "    y_pred_bagging\n",
+    "\n",
+    "    from costcla.metrics import savings_score, cost_loss \n",
+    "\n",
+    "    cost_loss1=cost_loss(y_test, y_pred_bagging, cost_mat_test)\n",
+    "\n",
+    "    cost_loss2=cost_loss(y_test, y_pred_metacost, cost_mat_test)\n",
+    "    \n",
+    "    cost_loss_tot=cost_loss1-cost_loss2\n",
+    "\n",
+    "    res[w-1] =cost_loss_tot\n",
+    "    res1[w-1]=cost_loss1\n",
+    "    res2[w-1]=cost_loss2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RESULTADOS METACOST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150477.54279500002"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_loss2=cost_loss(y_test, y_pred_metacost, cost_mat_test)\n",
+    "cost_loss2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Valor Metacost</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>140836.642812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>141260.928524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>146430.499941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>147687.094226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>142435.642808</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Valor Metacost\n",
+       "0   140836.642812\n",
+       "1   141260.928524\n",
+       "2   146430.499941\n",
+       "3   147687.094226\n",
+       "4   142435.642808"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res2=DataFrame(res2,columns=['Valor Metacost'])\n",
+    "res2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Metacost    139281.471377\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res2.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Metacost    8\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res2.idxmin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "minimun=139281.471377"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RESULTADOS BAGGING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "190800.00001999998"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " cost_loss1=cost_loss(y_test, y_pred_bagging, cost_mat_test)\n",
+    " cost_loss1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Valor Bagging</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>190829.428591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>190800.000020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>191067.428592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>190800.000020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>190800.000020</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Valor Bagging\n",
+       "0  190829.428591\n",
+       "1  190800.000020\n",
+       "2  191067.428592\n",
+       "3  190800.000020\n",
+       "4  190800.000020"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res1=DataFrame(res1,columns=['Valor Bagging'])\n",
+    "res1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Bagging    190800.00002\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res1.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Bagging    1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res1.idxmin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AHORRO METACOST-BAGGING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Valor Ahorro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>49992.785779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>49539.071496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>44636.928651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>43112.905794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48364.357212</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Valor Ahorro\n",
+       "0  49992.785779\n",
+       "1  49539.071496\n",
+       "2  44636.928651\n",
+       "3  43112.905794\n",
+       "4  48364.357212"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res=DataFrame(res,columns=['Valor Ahorro'])\n",
+    "res.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Ahorro    51518.528643\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Ahorro    76\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res.idxmin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# REGRESIÓN LOGÍSTICA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=10000000.0, class_weight=None, dual=False,\n",
+       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
+       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
+       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression(C=1e7)\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "y_pred_log=logreg.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "minimunlog=cost_loss(y_test, y_pred_log, cost_mat_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "190800.00001999998"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "minimunlog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AHORRO METACOST- REGRESIÓN LOGÍSTICA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51518.528642999969"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "minimunlog-minimun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# RANDOM FOREST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "res3=np.zeros(100)\n",
+    "ran=10\n",
+    "\n",
+    "for r in range (1,101):\n",
+    "    \n",
+    "    ran=ran+10\n",
+    "    rfclas = RandomForestClassifier(n_estimators=1000,random_state=ran, n_jobs=-1)\n",
+    "    rfclas.fit(X_train,y_train)\n",
+    "    y_pred_ran = rfclas.predict(X_test)\n",
+    "\n",
+    "    cost_loss1=cost_loss(y_test, y_pred_ran, cost_mat_test)\n",
+    "    res3[r-1] =cost_loss1\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "191768.17144599999"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_loss(y_test, y_pred_ran, cost_mat_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Valor Ahorro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>191689.885732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>191554.742875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>191640.742875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>191694.171446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>191640.742875</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Valor Ahorro\n",
+       "0  191689.885732\n",
+       "1  191554.742875\n",
+       "2  191640.742875\n",
+       "3  191694.171446\n",
+       "4  191640.742875"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res3=DataFrame(res3,columns=['Valor Ahorro'])\n",
+    "res3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Ahorro    191468.742875\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res3.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Ahorro    41\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res3.idxmin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AHORRO METACOST-RADOM FOREST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "52272.52862299999"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "191554-minimun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NAYVE BAYES "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "gnb = GaussianNB()\n",
+    "gnb.fit(X_train,y_train)\n",
+    "y_pred_navy=gnb.predict(X_test)\n",
+    "pred = gnb.predict_proba(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "247910.46270899998"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_loss(y_test, y_pred_navy, cost_mat_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AHORRO METACOST-NAYVE BAYES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "52273.27149799999"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "191554.742875-139281.471377"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KNEIGHBORS CLASSIFIER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "res4=np.zeros(40)\n",
+    "ran=20\n",
+    "\n",
+    "for r in range (1,41):\n",
+    "    ran=ran+150\n",
+    "    knn = KNeighborsClassifier(n_neighbors=ran)\n",
+    "    knn.fit(X_train, y_train)\n",
+    "    y_pred_knn=knn.predict(X_test)\n",
+    "    pred = knn.predict_proba(X_test)\n",
+    "    cost_loss1=cost_loss(y_test, y_pred_knn, cost_mat_test)\n",
+    "    res4[r-1] =cost_loss1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Valor Ahorro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>190800.00002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>190800.00002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>190800.00002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>190800.00002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>190800.00002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Valor Ahorro\n",
+       "0  190800.00002\n",
+       "1  190800.00002\n",
+       "2  190800.00002\n",
+       "3  190800.00002\n",
+       "4  190800.00002"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res4=DataFrame(res4,columns=['Valor Ahorro'])\n",
+    "res4.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Ahorro    190800.00002\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res4.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Valor Ahorro    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res4.idxmin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AHORRO METACOST-KNEIGHBORS CLASSIFIER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "190800.00002-139281.471377"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EVALUACION DE MODELOS "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# METACOST - BAGGING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\freddialexsander\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\bagging.py:537: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    }
+   ],
+   "source": [
+    " from sklearn.ensemble import BaggingClassifier\n",
+    "\n",
+    "dt = DecisionTreeClassifier(max_depth=4,random_state=42)\n",
+    "bagreg = BaggingClassifier(dt, n_estimators=9, \n",
+    "                              bootstrap=True, oob_score=True, random_state=1)\n",
+    "\n",
+    "bagreg.fit(X_train, y_train)\n",
+    "    \n",
+    "y_pred = bagreg.predict_proba(X_test)\n",
+    "\n",
+    "    # 'C_FP','C_FN','C_TP','C_TN'\n",
+    "\n",
+    "y_pred_metacost = np.zeros(X_test.shape[0])\n",
+    "\n",
+    "\n",
+    "for i in range (1,3096):\n",
+    "        c_j0 = y_pred[i, 0] * cost_mat_test[i, 3] + y_pred[i, 1]* cost_mat_test[i, 1]\n",
+    "        c_j1 = y_pred[i, 0] * cost_mat_test[i, 0] + y_pred[i, 1]* cost_mat_test[i, 2]\n",
+    "    \n",
+    "        if c_j0 > c_j1:\n",
+    "            y_pred_metacost[i] = 1\n",
+    "\n",
+    "y_pred_bagging = bagreg.predict(X_test)\n",
+    "\n",
+    "y_pred_bagging\n",
+    "\n",
+    "from costcla.metrics import savings_score, cost_loss \n",
+    "\n",
+    "cost_loss1=cost_loss(y_test, y_pred_bagging, cost_mat_test)\n",
+    "cost_loss2=cost_loss(y_test, y_pred_metacost, cost_mat_test)\n",
+    "    \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "190800.00001999998"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_loss1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.metrics import f1_score\n",
+    "from sklearn.metrics import log_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a=accuracy_score(y_test, y_pred_metacost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96965116279069696"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.19441770933589989"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, y_pred_metacost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.3377197442290782"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_loss(y_test, y_pred_metacost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94928940568475451"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred_bagging)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, y_pred_bagging)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.7514818779072931"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_loss(y_test, y_pred_bagging)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# REGRESIÓN LOGISTICA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=10000000.0, class_weight=None, dual=False,\n",
+       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
+       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
+       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "logreg = LogisticRegression(C=1e7)\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_log=logreg.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94928940568475451"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred_log)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, y_pred_log)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.7514818779072931"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_loss(y_test, y_pred_log)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RANDOM FOREST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "rfclas = RandomForestClassifier(n_estimators=1000,random_state=120, n_jobs=-1)\n",
+    "rfclas.fit(X_train,y_train)\n",
+    "y_pred_ran = rfclas.predict(X_test)\n",
+    "\n",
+    "cost_loss1=cost_loss(y_test, y_pred_ran, cost_mat_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "191554.742875"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cost_loss1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94186046511627908"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred_ran)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01098901098901099"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, y_pred_ran)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0080745934826387"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_loss(y_test, y_pred_ran)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NAVY BAYES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "gnb = GaussianNB()\n",
+    "gnb.fit(X_train,y_train)\n",
+    "y_pred_navy=gnb.predict(X_test)\n",
+    "pred = gnb.predict_proba(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.11531007751937984"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred_navy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.099309437684972024"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, y_pred_navy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30.556813257570116"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_loss(y_test, y_pred_navy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KNEIGHBORS CLASSIFIER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=160)\n",
+    "knn.fit(X_train, y_train)\n",
+    "y_pred_knn=knn.predict(X_test)\n",
+    "pred = knn.predict_proba(X_test)\n",
+    "cost_loss1=cost_loss(y_test, y_pred_knn, cost_mat_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.94928940568475451"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test, y_pred_knn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fbaron\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_test, y_pred_knn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.7514818779072931"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_loss(y_test, y_pred_knn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GRAFÍCAS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "Modelos = (\"MetaCost\",\"Bagging\",\"R.Log\", \"R.Forest\", \"N.Bayes\",\"knn\")\n",
+    "posicion_y = np.arange(len(Modelos))\n",
+    "unidades = (139281.471377, 190800.00002,190800.00001999998,191468.742875,247910.46270899998, 190800.00002)\n",
+    "plt.barh(posicion_y, unidades, align = \"center\")\n",
+    "plt.yticks(posicion_y, paises)\n",
+    "plt.xlabel(\"DOLARES\")\n",
+    "plt.title(\"COSTO MÍNIMO\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Modelos = (\"MetaCost\",\"Bagging\",\"R.Log\", \"R.Forest\", \"N.Bayes\",\"knn\")\n",
+    "posicion_y = np.arange(len(Modelos))\n",
+    "unidades = (0.96965116279069696, 0.94928940568475451,0.94928940568475451,0.94186046511627908,0.11531007751937984,0.94928940568475451)\n",
+    "plt.barh(posicion_y, unidades, align = \"center\")\n",
+    "plt.yticks(posicion_y, paises)\n",
+    "plt.xlabel('ACCURACY')\n",
+    "plt.title(\"EVALUACIÓN DE MODELOS\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}