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lab-problems-with-ML/Yidi #41

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986 changes: 966 additions & 20 deletions your-code/lab_boston_housing.ipynb
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186 changes: 169 additions & 17 deletions your-code/lab_overfitting.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -56,11 +56,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('svc', SVC(gamma='auto'))])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here\n"
"# Your code here\n",
"import numpy as np\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.svm import SVC\n",
"from sklearn.pipeline import Pipeline\n",
"classifiers = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
"classifiers.fit(X, y)\n",
"Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('svc', SVC(gamma='auto'))])"
]
},
{
Expand All @@ -72,9 +93,33 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "AttributeError",
"evalue": "'StandardScaler' object has no attribute 'predict_proba'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-40dff52d3498>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;31m# Put the result into a color plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'StandardScaler' object has no attribute 'predict_proba'"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAFpCAYAAAA/Y/sMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPKElEQVR4nO3cX4jld3nH8c9jtqnU+qc0K0j+mEjX6iIF7ZBahKpoS5KL5MZKAmItwUXb2AulkGKxEq9qaQUhrS6tWAWN0QtdZEtKbcQixmZFjSaSso3WLJFm/dPciMbQpxdnKsNkNnOyOfMsc/b1goXzO+c7Z55vZvadX845v1R3B4AZTzvXAwCcT0QXYJDoAgwSXYBBogswSHQBBu0a3ar6UFU9XFXfPMPjVVXvr6qTVXVPVb1s9WMCrIdlznQ/nOSqJ3j86iSHNv8cSfJ3T30sgPW0a3S7+wtJfvgES65L8pFeuCvJc6rqeasaEGCdrOI13YuTPLjl+NTmfQBsc2AFz1E73LfjtcVVdSSLlyDyjGc84zdf9KIXreDbA8z6yle+8v3uPng2X7uK6J5KcumW40uSPLTTwu4+muRokmxsbPSJEydW8O0BZlXVf53t167i5YVjSd64+SmGlyd5pLu/t4LnBVg7u57pVtXHk7wqyUVVdSrJXyT5hSTp7g8kOZ7kmiQnk/w4yR/u1bAA+92u0e3uG3Z5vJP88comAlhjrkgDGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsCgpaJbVVdV1f1VdbKqbt7h8cuq6s6q+mpV3VNV16x+VID9b9foVtUFSW5NcnWSw0luqKrD25b9eZLbu/ulSa5P8rerHhRgHSxzpntlkpPd/UB3P5rktiTXbVvTSZ61efvZSR5a3YgA6+PAEmsuTvLgluNTSX5r25p3J/nnqnpbkmckee1KpgNYM8uc6dYO9/W24xuSfLi7L0lyTZKPVtXjnruqjlTViao6cfr06Sc/LcA+t0x0TyW5dMvxJXn8ywc3Jrk9Sbr7S0menuSi7U/U3Ue7e6O7Nw4ePHh2EwPsY8tE9+4kh6rqiqq6MIs3yo5tW/PdJK9Jkqp6cRbRdSoLsM2u0e3ux5LclOSOJN/K4lMK91bVLVV17eaydyR5c1V9PcnHk7ypu7e/BAFw3lvmjbR09/Ekx7fd964tt+9L8orVjgawflyRBjBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMGip6FbVVVV1f1WdrKqbz7Dm9VV1X1XdW1UfW+2YAOvhwG4LquqCJLcm+d0kp5LcXVXHuvu+LWsOJfmzJK/o7h9V1XP3amCA/WyZM90rk5zs7ge6+9EktyW5btuaNye5tbt/lCTd/fBqxwRYD8tE9+IkD245PrV531YvTPLCqvpiVd1VVVft9ERVdaSqTlTVidOnT5/dxAD72DLRrR3u623HB5IcSvKqJDck+fuqes7jvqj7aHdvdPfGwYMHn+ysAPveMtE9leTSLceXJHlohzWf6e6fdfe3k9yfRYQB2GKZ6N6d5FBVXVFVFya5PsmxbWs+neTVSVJVF2XxcsMDqxwUYB3sGt3ufizJTUnuSPKtJLd3971VdUtVXbu57I4kP6iq+5LcmeRPu/sHezU0wH5V3dtfnp2xsbHRJ06cOCffG+CpqKqvdPfG2XytK9IABokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBoqehW1VVVdX9Vnayqm59g3euqqqtqY3UjAqyPXaNbVRckuTXJ1UkOJ7mhqg7vsO6ZSf4kyZdXPSTAuljmTPfKJCe7+4HufjTJbUmu22Hde5K8N8lPVjgfwFpZJroXJ3lwy/Gpzft+rqpemuTS7v7sEz1RVR2pqhNVdeL06dNPeliA/W6Z6NYO9/XPH6x6WpL3JXnHbk/U3Ue7e6O7Nw4ePLj8lABrYpnonkpy6ZbjS5I8tOX4mUlekuTzVfWdJC9PcsybaQCPt0x0705yqKquqKoLk1yf5Nj/P9jdj3T3Rd19eXdfnuSuJNd294k9mRhgH9s1ut39WJKbktyR5FtJbu/ue6vqlqq6dq8HBFgnB5ZZ1N3Hkxzfdt+7zrD2VU99LID15Io0gEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMWiq6VXVVVd1fVSer6uYdHn97Vd1XVfdU1eeq6vmrHxVg/9s1ulV1QZJbk1yd5HCSG6rq8LZlX02y0d2/keRTSd676kEB1sEyZ7pXJjnZ3Q9096NJbkty3dYF3X1nd/948/CuJJesdkyA9bBMdC9O8uCW41Ob953JjUn+6akMBbCuDiyxpna4r3dcWPWGJBtJXnmGx48kOZIkl1122ZIjAqyPZc50TyW5dMvxJUke2r6oql6b5J1Jru3un+70RN19tLs3unvj4MGDZzMvwL62THTvTnKoqq6oqguTXJ/k2NYFVfXSJB/MIrgPr35MgPWwa3S7+7EkNyW5I8m3ktze3fdW1S1Vde3msr9K8stJPllVX6uqY2d4OoDz2jKv6aa7jyc5vu2+d225/doVzwWwllyRBjBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQaILMEh0AQaJLsAg0QUYJLoAg0QXYJDoAgwSXYBBogswSHQBBokuwCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEGiS7AINEFGCS6AINEF2CQ6AIMEl2AQUtFt6quqqr7q+pkVd28w+O/WFWf2Hz8y1V1+aoHBVgHu0a3qi5IcmuSq5McTnJDVR3etuzGJD/q7l9L8r4kf7nqQQHWwTJnulcmOdndD3T3o0luS3LdtjXXJfnHzdufSvKaqqrVjQmwHpaJ7sVJHtxyfGrzvh3XdPdjSR5J8qurGBBgnRxYYs1OZ6x9FmtSVUeSHNk8/GlVfXOJ779OLkry/XM9xDB7Pj+cb3v+9bP9wmWieyrJpVuOL0ny0BnWnKqqA0meneSH25+ou48mOZokVXWiuzfOZuj9yp7PD/a8/qrqxNl+7TIvL9yd5FBVXVFVFya5PsmxbWuOJfmDzduvS/Kv3f24M12A892uZ7rd/VhV3ZTkjiQXJPlQd99bVbckOdHdx5L8Q5KPVtXJLM5wr9/LoQH2q2VeXkh3H09yfNt979py+ydJfv9Jfu+jT3L9OrDn84M9r7+z3m95FQBgjsuAAQbteXTPx0uIl9jz26vqvqq6p6o+V1XPPxdzrtJue96y7nVV1VW1r9/pXma/VfX6zZ/zvVX1sekZV22J3+vLqurOqvrq5u/2NedizlWqqg9V1cNn+nhrLbx/85/JPVX1sl2ftLv37E8Wb7z9Z5IXJLkwydeTHN625o+SfGDz9vVJPrGXM+31nyX3/Ookv7R5+63nw5431z0zyReS3JVk41zPvcc/40NJvprkVzaPn3uu5x7Y89Ekb928fTjJd8713CvY9+8keVmSb57h8WuS/FMW1yq8PMmXd3vOvT7TPR8vId51z919Z3f/ePPwriw++7yfLfNzTpL3JHlvkp9MDrcHltnvm5Pc2t0/SpLufnh4xlVbZs+d5Fmbt5+dx3+ef9/p7i9kh2sOtrguyUd64a4kz6mq5z3Rc+51dM/HS4iX2fNWN2bxb8r9bNc9V9VLk1za3Z+dHGyPLPMzfmGSF1bVF6vqrqq6amy6vbHMnt+d5A1VdSqLTzu9bWa0c+rJ/n1f7iNjT8HKLiHeR5beT1W9IclGklfu6UR77wn3XFVPy+L/PvemqYH22DI/4wNZvMTwqiz+S+bfquol3f0/ezzbXllmzzck+XB3/3VV/XYWn91/SXf/796Pd8486X7t9Znuk7mEOE90CfE+ssyeU1WvTfLOJNd290+HZtsru+35mUlekuTzVfWdLF77OraP30xb9vf6M939s+7+dpL7s4jwfrXMnm9McnuSdPeXkjw9i/8nwzpb6u/7Vnsd3fPxEuJd97z5n9ofzCK4+/21vmSXPXf3I919UXdf3t2XZ/E69rXdfdbXr59jy/xefzqLN0xTVRdl8XLDA6NTrtYye/5uktckSVW9OIvonh6dct6xJG/c/BTDy5M80t3fe8KvGHj375ok/5HFO5/v3Lzvliz+0iWLH8wnk5xM8u9JXnCu37Ec2PO/JPnvJF/b/HPsXM+813vetvbz2cefXljyZ1xJ/ibJfUm+keT6cz3zwJ4PJ/liFp9s+FqS3zvXM69gzx9P8r0kP8virPbGJG9J8pYtP+dbN/+ZfGOZ32tXpAEMckUawCDRBRgkugCDRBdgkOgCDBJdgEGiCzBIdAEG/R+B5SSfra5IVQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"\n",
Expand All @@ -86,7 +131,8 @@
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"cm = plt.cm.RdBu\n",
"cm_bright = ListedColormap(['#FF0000', '#0000FF'])\nnames = ['gamma = 0.001', 'gamma = 1', 'gamma = 20']\n",
"cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
"names = ['gamma = 0.001', 'gamma = 1', 'gamma = 20']\n",
"\n",
"# iterate over classifiers\n",
"for name, clf in zip(names, classifiers):\n",
Expand Down Expand Up @@ -146,11 +192,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.5125"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here"
"# Your code here\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.8,random_state=8)\n",
"from sklearn import svm\n",
"clf = svm.SVC(kernel='linear')\n",
"clf.fit(X_train, y_train).score(X_test, y_test)"
]
},
{
Expand All @@ -162,11 +224,50 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC(gamma=20))])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here"
"# Your code here\n",
"classifiers20 = make_pipeline(StandardScaler(), SVC(gamma=20))\n",
"classifiers20.fit(X, y)\n",
"Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('svc', SVC(gamma=20))])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5125"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.8,random_state=8)\n",
"from sklearn import svm\n",
"clf20 = svm.SVC(kernel='linear')\n",
"clf20.fit(X_train, y_train).score(X_test, y_test)"
]
},
{
Expand All @@ -178,11 +279,49 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC(gamma=1000))])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here\n",
"classifiers1000 = make_pipeline(StandardScaler(), SVC(gamma=1000))\n",
"classifiers1000.fit(X, y)\n",
"Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('svc', SVC(gamma=1000))])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5125"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Your code here"
"X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.8,random_state=8)\n",
"from sklearn import svm\n",
"clf1000= svm.SVC(kernel='linear')\n",
"clf1000.fit(X_train, y_train).score(X_test, y_test)"
]
},
{
Expand Down Expand Up @@ -218,7 +357,20 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.7.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
Expand Down