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Fix sklearn.model_selection. Remove output

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dveni-patch-1
J. Fernando Sánchez 5 years ago
parent 4d339a1a83
commit a1be167cc0
13 changed files with 134 additions and 1371 deletions
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2
ml1/2_0_0_Intro_ML.ipynb
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@ -102,7 +102,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
"version": "3.6.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

2
ml1/2_0_1_Objectives.ipynb
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@ -95,7 +95,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
"version": "3.6.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

2
ml1/2_1_Intro_ScikitLearn.ipynb
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@ -178,7 +178,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
"version": "3.6.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

8
ml1/2_3_0_Visualisation.ipynb
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@ -161,9 +161,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"metadata": {},
"outputs": [],
"source": [
"# Plot the distribution of the dataset\n",
@ -192,9 +190,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"metadata": {},
"outputs": [],
"source": [
"x_index = 0\n",

278
ml1/2_3_1_Advanced_Visualisation.ipynb
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@ -93,90 +93,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pandas import DataFrame\n",
"from sklearn import datasets\n",
@ -194,103 +113,9 @@
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
"0 5.1 3.5 1.4 0.2 \n",
"1 4.9 3.0 1.4 0.2 \n",
"2 4.7 3.2 1.3 0.2 \n",
"3 4.6 3.1 1.5 0.2 \n",
"4 5.0 3.6 1.4 0.2 \n",
"\n",
" species \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iris_df['species'] = iris.target\n",
"iris_df.head()"
@ -328,33 +153,9 @@
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x7f2a3c2b60f0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\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[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
@ -383,23 +184,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\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[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# PairGrid\n",
"g = sns.PairGrid(iris_df)\n",
@ -417,38 +204,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'numpy.int64' object is not iterable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-322fd70587c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPairGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miris_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"species\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_offdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#names = {i: name for i,name in enumerate(iris.target_names)}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#g.add_legend(legend_data=names)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 1397\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfixed_color\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1401\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 2956\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2957\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2958\u001b[0;31m stacked=stacked, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 2959\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2960\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1810\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1811\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1812\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1813\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1814\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m 6216\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6217\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6218\u001b[0;31m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlab\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlab\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6220\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlbl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip_longest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'numpy.int64' object is not iterable"
]
},
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m )\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\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[0m\u001b[1;32m 175\u001b[0m \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m ticksLight = _is_light([label.get_color()\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(iris_df, hue=\"species\")\n",
"g.map_diag(plt.hist)\n",
@ -485,9 +243,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(iris_df)\n",
@ -688,7 +444,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
"version": "3.6.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

199
ml1/2_5_1_kNN_Model.ipynb
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ml1/2_5_2_Decision_Tree_Model.ipynb
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795
ml1/2_6_Model_Tuning.ipynb
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@ -70,7 +70,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -101,9 +101,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"## Train classifier"
]
@ -117,17 +115,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean score: 0.940 (+/- 0.021)\n"
]
}
],
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score, KFold\n",
"from sklearn.pipeline import Pipeline\n",
@ -179,51 +169,18 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'ds': DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'),\n",
" 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True)}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"model.named_steps"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
" ('ds',\n",
" DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"model.steps"
]
@ -237,20 +194,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['steps', 'scaler', 'ds', 'scaler__copy', 'scaler__with_mean', 'scaler__with_std', 'ds__class_weight', 'ds__criterion', 'ds__max_depth', 'ds__max_features', 'ds__max_leaf_nodes', 'ds__min_impurity_split', 'ds__min_samples_leaf', 'ds__min_samples_split', 'ds__min_weight_fraction_leaf', 'ds__presort', 'ds__random_state', 'ds__splitter'])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"model.get_params().keys()"
]
@ -264,24 +210,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"model.set_params(ds__class_weight='balanced')"
]
@ -295,24 +226,9 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"model = Pipeline([\n",
" ('scaler', StandardScaler()),\n",
@ -330,17 +246,9 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.01834862 0.01910853 0.05728223 0.90526062]\n"
]
}
],
"outputs": [],
"source": [
"# Fit the model\n",
"model.fit(x_train, y_train) \n",
@ -351,17 +259,9 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.01834862 0.01910853 0.05728223 0.90526062]\n"
]
}
],
"outputs": [],
"source": [
"#Using steps, we take the last step (-1) or the second step (1)\n",
"#name, my_desision_tree = model.steps[1]\n",
@ -389,47 +289,9 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'ds': DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'),\n",
" 'ds__class_weight': 'balanced',\n",
" 'ds__criterion': 'gini',\n",
" 'ds__max_depth': None,\n",
" 'ds__max_features': None,\n",
" 'ds__max_leaf_nodes': None,\n",
" 'ds__min_impurity_split': 1e-07,\n",
" 'ds__min_samples_leaf': 1,\n",
" 'ds__min_samples_split': 2,\n",
" 'ds__min_weight_fraction_leaf': 0.0,\n",
" 'ds__presort': False,\n",
" 'ds__random_state': None,\n",
" 'ds__splitter': 'best',\n",
" 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True),\n",
" 'scaler__copy': True,\n",
" 'scaler__with_mean': True,\n",
" 'scaler__with_std': True,\n",
" 'steps': [('scaler',\n",
" StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
" ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))]}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"model.get_params()"
]
@ -466,18 +328,9 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best score: 0.946428571429\n",
"Best params: {'max_depth': 3}\n"
]
}
],
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.tree import DecisionTreeClassifier\n",
@ -496,32 +349,16 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"Now we are going to show the results of grid search"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.946 (+/-0.075) for {'max_depth': 3}\n",
"0.929 (+/-0.024) for {'max_depth': 4}\n",
"0.946 (+/-0.075) for {'max_depth': 5}\n",
"0.929 (+/-0.024) for {'max_depth': 6}\n",
"0.946 (+/-0.075) for {'max_depth': 7}\n",
"0.946 (+/-0.075) for {'max_depth': 8}\n",
"0.929 (+/-0.024) for {'max_depth': 9}\n"
]
}
],
"outputs": [],
"source": [
"# We print the score for each value of max_depth\n",
"for i, max_depth in enumerate(gs.cv_results_['params']):\n",
@ -539,17 +376,9 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean score: 0.953 (+/- 0.020)\n"
]
}
],
"outputs": [],
"source": [
"# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
"model = Pipeline([\n",
@ -581,550 +410,9 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Tuning hyper-parameters for precision\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters set found on development set:\n",
"\n",
"{'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"\n",
"Grid scores on development set:\n",
"\n",
"0.964 (+/-0.092) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.943 (+/-0.084) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.936 (+/-0.122) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.973 (+/-0.068) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.968 (+/-0.132) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.943 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.919 (+/-0.251) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.975 (+/-0.079) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.951 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.943 (+/-0.113) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.948 (+/-0.108) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.961 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.928 (+/-0.165) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.949 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.953 (+/-0.134) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.942 (+/-0.067) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.980 (+/-0.062) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.945 (+/-0.141) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.949 (+/-0.095) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.961 (+/-0.114) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.972 (+/-0.069) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.953 (+/-0.126) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.946 (+/-0.125) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.938 (+/-0.142) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.956 (+/-0.121) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.968 (+/-0.082) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.956 (+/-0.097) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.906 (+/-0.296) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.932 (+/-0.110) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.955 (+/-0.121) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.921 (+/-0.132) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.942 (+/-0.132) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.948 (+/-0.108) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.945 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.897 (+/-0.187) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.944 (+/-0.148) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.948 (+/-0.107) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.961 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.939 (+/-0.117) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.949 (+/-0.090) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.972 (+/-0.068) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.906 (+/-0.162) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.947 (+/-0.146) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.950 (+/-0.118) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.946 (+/-0.123) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.953 (+/-0.134) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.975 (+/-0.079) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.932 (+/-0.136) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.940 (+/-0.146) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.953 (+/-0.082) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.979 (+/-0.064) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.952 (+/-0.108) for {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.968 (+/-0.082) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.919 (+/-0.106) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.941 (+/-0.129) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.956 (+/-0.094) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.954 (+/-0.154) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.949 (+/-0.158) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.893 (+/-0.163) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.916 (+/-0.186) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.961 (+/-0.081) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.947 (+/-0.108) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.912 (+/-0.120) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.960 (+/-0.082) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.962 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.966 (+/-0.070) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.962 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.949 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.954 (+/-0.112) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.955 (+/-0.097) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.974 (+/-0.081) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.947 (+/-0.175) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.950 (+/-0.117) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.935 (+/-0.075) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.954 (+/-0.129) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.940 (+/-0.142) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.934 (+/-0.155) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.927 (+/-0.112) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.934 (+/-0.184) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.932 (+/-0.136) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.968 (+/-0.082) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.903 (+/-0.240) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.939 (+/-0.179) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.975 (+/-0.079) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.923 (+/-0.094) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.967 (+/-0.083) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.944 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.938 (+/-0.177) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.964 (+/-0.092) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.950 (+/-0.117) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.895 (+/-0.229) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.944 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.930 (+/-0.199) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.953 (+/-0.126) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.949 (+/-0.116) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.922 (+/-0.177) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.959 (+/-0.067) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.933 (+/-0.136) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.933 (+/-0.125) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.939 (+/-0.117) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.946 (+/-0.123) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.918 (+/-0.155) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.945 (+/-0.123) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.931 (+/-0.153) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.944 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.972 (+/-0.069) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.968 (+/-0.082) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.955 (+/-0.111) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"\n",
"Detailed classification report:\n",
"\n",
"The model is trained on the full development set.\n",
"The scores are computed on the full evaluation set.\n",
"\n",
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 8\n",
" 1 0.92 1.00 0.96 11\n",
" 2 1.00 0.95 0.97 19\n",
"\n",
"avg / total 0.98 0.97 0.97 38\n",
"\n",
"\n",
"# Tuning hyper-parameters for recall\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
" DeprecationWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters set found on development set:\n",
"\n",
"{'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"\n",
"Grid scores on development set:\n",
"\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.893 (+/-0.215) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.955 (+/-0.092) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.938 (+/-0.138) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.920 (+/-0.241) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.946 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.866 (+/-0.268) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.884 (+/-0.218) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.911 (+/-0.179) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.973 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.893 (+/-0.177) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.920 (+/-0.162) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.920 (+/-0.187) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.929 (+/-0.104) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
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"0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.938 (+/-0.163) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.938 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.920 (+/-0.187) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.920 (+/-0.187) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.929 (+/-0.131) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.929 (+/-0.155) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.920 (+/-0.127) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.938 (+/-0.159) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.938 (+/-0.137) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.902 (+/-0.179) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.929 (+/-0.175) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.902 (+/-0.148) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.955 (+/-0.146) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.955 (+/-0.169) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.964 (+/-0.121) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.929 (+/-0.136) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n",
"0.920 (+/-0.147) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n",
"0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n",
"0.938 (+/-0.137) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n",
"0.929 (+/-0.168) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n",
"0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n",
"0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n",
"\n",
"Detailed classification report:\n",
"\n",
"The model is trained on the full development set.\n",
"The scores are computed on the full evaluation set.\n",
"\n",
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 8\n",
" 1 1.00 0.64 0.78 11\n",
" 2 0.83 1.00 0.90 19\n",
"\n",
"avg / total 0.91 0.89 0.89 38\n",
"\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
" DeprecationWarning)\n"
]
}
],
"outputs": [],
"source": [
"# Set the parameters by cross-validation\n",
"\n",
@ -1156,8 +444,11 @@
" print()\n",
" print(\"Grid scores on development set:\")\n",
" print()\n",
" for params, mean_score, scores in gs.grid_scores_:\n",
" print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, scores.std() * 2, params))\n",
" means = gs.cv_results_['mean_test_score']\n",
" stds = gs.cv_results_['std_test_score']\n",
"\n",
" for mean_score, std_score, params in zip(means, stds, gs.cv_results_['params']):\n",
" print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, std_score * 2, params))\n",
" print()\n",
"\n",
" print(\"Detailed classification report:\")\n",
@ -1172,26 +463,16 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"Let's evaluate the resulting tuning."
]
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean score: 0.907 (+/- 0.015)\n"
]
}
],
"outputs": [],
"source": [
"# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
"model = Pipeline([\n",
@ -1271,7 +552,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
"version": "3.6.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

40
ml1/2_7_Model_Persistence.ipynb
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@ -55,22 +55,9 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('KNN', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
" weights='uniform'))])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"# load iris\n",
"from sklearn import datasets\n",
@ -106,20 +93,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"import pickle\n",
"s = pickle.dumps(model)\n",
@ -136,10 +112,8 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# save model\n",
@ -192,7 +166,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
"version": "3.6.7"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,

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ml1/plotml.py
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@ -1,7 +1,7 @@
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier

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