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environment.yml
name: example-environment
channels:
- conda-forge
dependencies:
- python
- numpy
- graphviz
- pip:
- nbgitpuller
- sphinx-gallery
- pandas
- matplotlib
- sklearn
- pydotplus
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hercodecamp_day2-noanswers.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h3>Building a Cancer Classifier using Random Forest</h3>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>1- Load The Required Packages</h4>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"import pandas as pd #data manupilation\n",
"from sklearn.model_selection import train_test_split #splitting the data to train and test\n",
"from sklearn import tree #running a decision tree\n",
"from sklearn.ensemble import RandomForestClassifier #running a random forest\n",
"from sklearn import datasets #saved datasets\n",
"\n",
"from sklearn import metrics #assessing model performance\n",
"from sklearn.metrics import classification_report #assessing model performance\n",
"from sklearn.metrics import confusion_matrix #assessing model performance\n",
"import matplotlib.pyplot as plt #visualize model performance\n",
"\n",
"pd.set_option('display.max_columns', 30) #display all columns in your data"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>2- Load The Data</h4>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>radius error</th>\n",
" <th>texture error</th>\n",
" <th>perimeter error</th>\n",
" <th>area error</th>\n",
" <th>smoothness error</th>\n",
" <th>compactness error</th>\n",
" <th>concavity error</th>\n",
" <th>concave points error</th>\n",
" <th>symmetry error</th>\n",
" <th>fractal dimension error</th>\n",
" <th>worst radius</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
" <th>worst symmetry</th>\n",
" <th>worst fractal dimension</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.3001</td>\n",
" <td>0.14710</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>1.0950</td>\n",
" <td>0.9053</td>\n",
" <td>8.589</td>\n",
" <td>153.40</td>\n",
" <td>0.006399</td>\n",
" <td>0.04904</td>\n",
" <td>0.05373</td>\n",
" <td>0.01587</td>\n",
" <td>0.03003</td>\n",
" <td>0.006193</td>\n",
" <td>25.38</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.1622</td>\n",
" <td>0.6656</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.0869</td>\n",
" <td>0.07017</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>0.5435</td>\n",
" <td>0.7339</td>\n",
" <td>3.398</td>\n",
" <td>74.08</td>\n",
" <td>0.005225</td>\n",
" <td>0.01308</td>\n",
" <td>0.01860</td>\n",
" <td>0.01340</td>\n",
" <td>0.01389</td>\n",
" <td>0.003532</td>\n",
" <td>24.99</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.1238</td>\n",
" <td>0.1866</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.1974</td>\n",
" <td>0.12790</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
" <td>0.7456</td>\n",
" <td>0.7869</td>\n",
" <td>4.585</td>\n",
" <td>94.03</td>\n",
" <td>0.006150</td>\n",
" <td>0.04006</td>\n",
" <td>0.03832</td>\n",
" <td>0.02058</td>\n",
" <td>0.02250</td>\n",
" <td>0.004571</td>\n",
" <td>23.57</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.1444</td>\n",
" <td>0.4245</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.2414</td>\n",
" <td>0.10520</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>0.4956</td>\n",
" <td>1.1560</td>\n",
" <td>3.445</td>\n",
" <td>27.23</td>\n",
" <td>0.009110</td>\n",
" <td>0.07458</td>\n",
" <td>0.05661</td>\n",
" <td>0.01867</td>\n",
" <td>0.05963</td>\n",
" <td>0.009208</td>\n",
" <td>14.91</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.2098</td>\n",
" <td>0.8663</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.1980</td>\n",
" <td>0.10430</td>\n",
" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
" <td>0.7572</td>\n",
" <td>0.7813</td>\n",
" <td>5.438</td>\n",
" <td>94.44</td>\n",
" <td>0.011490</td>\n",
" <td>0.02461</td>\n",
" <td>0.05688</td>\n",
" <td>0.01885</td>\n",
" <td>0.01756</td>\n",
" <td>0.005115</td>\n",
" <td>22.54</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.1374</td>\n",
" <td>0.2050</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.3001 0.14710 0.2419 \n",
"1 0.07864 0.0869 0.07017 0.1812 \n",
"2 0.15990 0.1974 0.12790 0.2069 \n",
"3 0.28390 0.2414 0.10520 0.2597 \n",
"4 0.13280 0.1980 0.10430 0.1809 \n",
"\n",
" mean fractal dimension radius error texture error perimeter error \\\n",
"0 0.07871 1.0950 0.9053 8.589 \n",
"1 0.05667 0.5435 0.7339 3.398 \n",
"2 0.05999 0.7456 0.7869 4.585 \n",
"3 0.09744 0.4956 1.1560 3.445 \n",
"4 0.05883 0.7572 0.7813 5.438 \n",
"\n",
" area error smoothness error compactness error concavity error \\\n",
"0 153.40 0.006399 0.04904 0.05373 \n",
"1 74.08 0.005225 0.01308 0.01860 \n",
"2 94.03 0.006150 0.04006 0.03832 \n",
"3 27.23 0.009110 0.07458 0.05661 \n",
"4 94.44 0.011490 0.02461 0.05688 \n",
"\n",
" concave points error symmetry error fractal dimension error worst radius \\\n",
"0 0.01587 0.03003 0.006193 25.38 \n",
"1 0.01340 0.01389 0.003532 24.99 \n",
"2 0.02058 0.02250 0.004571 23.57 \n",
"3 0.01867 0.05963 0.009208 14.91 \n",
"4 0.01885 0.01756 0.005115 22.54 \n",
"\n",
" worst texture worst perimeter worst area worst smoothness worst compactness \\\n",
"0 17.33 184.60 2019.0 0.1622 0.6656 \n",
"1 23.41 158.80 1956.0 0.1238 0.1866 \n",
"2 25.53 152.50 1709.0 0.1444 0.4245 \n",
"3 26.50 98.87 567.7 0.2098 0.8663 \n",
"4 16.67 152.20 1575.0 0.1374 0.2050 \n",
"\n",
" worst concavity worst concave points worst symmetry worst fractal dimension \n",
"0 0.7119 0.2654 0.4601 0.11890 \n",
"1 0.2416 0.1860 0.2750 0.08902 \n",
"2 0.4504 0.2430 0.3613 0.08758 \n",
"3 0.6869 0.2575 0.6638 0.17300 \n",
"4 0.4000 0.1625 0.2364 0.07678 "
]
},
"execution_count": 2,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"cancer =\n",
"X= #define your features\n",
"Y= #define the target variable\n",
"X.head() #view the first few rows from your features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(569, 30)\n"
]
}
],
"source": [
"#print the dimensions of the dataset\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"MultiIndex([( 'mean radius',),\n",
" ( 'mean texture',),\n",
" ( 'mean perimeter',),\n",
" ( 'mean area',),\n",
" ( 'mean smoothness',),\n",
" ( 'mean compactness',),\n",
" ( 'mean concavity',),\n",
" ( 'mean concave points',),\n",
" ( 'mean symmetry',),\n",
" ( 'mean fractal dimension',),\n",
" ( 'radius error',),\n",
" ( 'texture error',),\n",
" ( 'perimeter error',),\n",
" ( 'area error',),\n",
" ( 'smoothness error',),\n",
" ( 'compactness error',),\n",
" ( 'concavity error',),\n",
" ( 'concave points error',),\n",
" ( 'symmetry error',),\n",
" ('fractal dimension error',),\n",
" ( 'worst radius',),\n",
" ( 'worst texture',),\n",
" ( 'worst perimeter',),\n",
" ( 'worst area',),\n",
" ( 'worst smoothness',),\n",
" ( 'worst compactness',),\n",
" ( 'worst concavity',),\n",
" ( 'worst concave points',),\n",
" ( 'worst symmetry',),\n",
" ('worst fractal dimension',)],\n",
" )"
]
},
"execution_count": 4,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#let's look at column names\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>radius error</th>\n",
" <th>texture error</th>\n",
" <th>perimeter error</th>\n",
" <th>area error</th>\n",
" <th>smoothness error</th>\n",
" <th>compactness error</th>\n",
" <th>concavity error</th>\n",
" <th>concave points error</th>\n",
" <th>symmetry error</th>\n",
" <th>fractal dimension error</th>\n",
" <th>worst radius</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
" <th>worst symmetry</th>\n",
" <th>worst fractal dimension</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>14.127292</td>\n",
" <td>19.289649</td>\n",
" <td>91.969033</td>\n",
" <td>654.889104</td>\n",
" <td>0.096360</td>\n",
" <td>0.104341</td>\n",
" <td>0.088799</td>\n",
" <td>0.048919</td>\n",
" <td>0.181162</td>\n",
" <td>0.062798</td>\n",
" <td>0.405172</td>\n",
" <td>1.216853</td>\n",
" <td>2.866059</td>\n",
" <td>40.337079</td>\n",
" <td>0.007041</td>\n",
" <td>0.025478</td>\n",
" <td>0.031894</td>\n",
" <td>0.011796</td>\n",
" <td>0.020542</td>\n",
" <td>0.003795</td>\n",
" <td>16.269190</td>\n",
" <td>25.677223</td>\n",
" <td>107.261213</td>\n",
" <td>880.583128</td>\n",
" <td>0.132369</td>\n",
" <td>0.254265</td>\n",
" <td>0.272188</td>\n",
" <td>0.114606</td>\n",
" <td>0.290076</td>\n",
" <td>0.083946</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>3.524049</td>\n",
" <td>4.301036</td>\n",
" <td>24.298981</td>\n",
" <td>351.914129</td>\n",
" <td>0.014064</td>\n",
" <td>0.052813</td>\n",
" <td>0.079720</td>\n",
" <td>0.038803</td>\n",
" <td>0.027414</td>\n",
" <td>0.007060</td>\n",
" <td>0.277313</td>\n",
" <td>0.551648</td>\n",
" <td>2.021855</td>\n",
" <td>45.491006</td>\n",
" <td>0.003003</td>\n",
" <td>0.017908</td>\n",
" <td>0.030186</td>\n",
" <td>0.006170</td>\n",
" <td>0.008266</td>\n",
" <td>0.002646</td>\n",
" <td>4.833242</td>\n",
" <td>6.146258</td>\n",
" <td>33.602542</td>\n",
" <td>569.356993</td>\n",
" <td>0.022832</td>\n",
" <td>0.157336</td>\n",
" <td>0.208624</td>\n",
" <td>0.065732</td>\n",
" <td>0.061867</td>\n",
" <td>0.018061</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>6.981000</td>\n",
" <td>9.710000</td>\n",
" <td>43.790000</td>\n",
" <td>143.500000</td>\n",
" <td>0.052630</td>\n",
" <td>0.019380</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.106000</td>\n",
" <td>0.049960</td>\n",
" <td>0.111500</td>\n",
" <td>0.360200</td>\n",
" <td>0.757000</td>\n",
" <td>6.802000</td>\n",
" <td>0.001713</td>\n",
" <td>0.002252</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.007882</td>\n",
" <td>0.000895</td>\n",
" <td>7.930000</td>\n",
" <td>12.020000</td>\n",
" <td>50.410000</td>\n",
" <td>185.200000</td>\n",
" <td>0.071170</td>\n",
" <td>0.027290</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.156500</td>\n",
" <td>0.055040</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>11.700000</td>\n",
" <td>16.170000</td>\n",
" <td>75.170000</td>\n",
" <td>420.300000</td>\n",
" <td>0.086370</td>\n",
" <td>0.064920</td>\n",
" <td>0.029560</td>\n",
" <td>0.020310</td>\n",
" <td>0.161900</td>\n",
" <td>0.057700</td>\n",
" <td>0.232400</td>\n",
" <td>0.833900</td>\n",
" <td>1.606000</td>\n",
" <td>17.850000</td>\n",
" <td>0.005169</td>\n",
" <td>0.013080</td>\n",
" <td>0.015090</td>\n",
" <td>0.007638</td>\n",
" <td>0.015160</td>\n",
" <td>0.002248</td>\n",
" <td>13.010000</td>\n",
" <td>21.080000</td>\n",
" <td>84.110000</td>\n",
" <td>515.300000</td>\n",
" <td>0.116600</td>\n",
" <td>0.147200</td>\n",
" <td>0.114500</td>\n",
" <td>0.064930</td>\n",
" <td>0.250400</td>\n",
" <td>0.071460</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>13.370000</td>\n",
" <td>18.840000</td>\n",
" <td>86.240000</td>\n",
" <td>551.100000</td>\n",
" <td>0.095870</td>\n",
" <td>0.092630</td>\n",
" <td>0.061540</td>\n",
" <td>0.033500</td>\n",
" <td>0.179200</td>\n",
" <td>0.061540</td>\n",
" <td>0.324200</td>\n",
" <td>1.108000</td>\n",
" <td>2.287000</td>\n",
" <td>24.530000</td>\n",
" <td>0.006380</td>\n",
" <td>0.020450</td>\n",
" <td>0.025890</td>\n",
" <td>0.010930</td>\n",
" <td>0.018730</td>\n",
" <td>0.003187</td>\n",
" <td>14.970000</td>\n",
" <td>25.410000</td>\n",
" <td>97.660000</td>\n",
" <td>686.500000</td>\n",
" <td>0.131300</td>\n",
" <td>0.211900</td>\n",
" <td>0.226700</td>\n",
" <td>0.099930</td>\n",
" <td>0.282200</td>\n",
" <td>0.080040</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>15.780000</td>\n",
" <td>21.800000</td>\n",
" <td>104.100000</td>\n",
" <td>782.700000</td>\n",
" <td>0.105300</td>\n",
" <td>0.130400</td>\n",
" <td>0.130700</td>\n",
" <td>0.074000</td>\n",
" <td>0.195700</td>\n",
" <td>0.066120</td>\n",
" <td>0.478900</td>\n",
" <td>1.474000</td>\n",
" <td>3.357000</td>\n",
" <td>45.190000</td>\n",
" <td>0.008146</td>\n",
" <td>0.032450</td>\n",
" <td>0.042050</td>\n",
" <td>0.014710</td>\n",
" <td>0.023480</td>\n",
" <td>0.004558</td>\n",
" <td>18.790000</td>\n",
" <td>29.720000</td>\n",
" <td>125.400000</td>\n",
" <td>1084.000000</td>\n",
" <td>0.146000</td>\n",
" <td>0.339100</td>\n",
" <td>0.382900</td>\n",
" <td>0.161400</td>\n",
" <td>0.317900</td>\n",
" <td>0.092080</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>28.110000</td>\n",
" <td>39.280000</td>\n",
" <td>188.500000</td>\n",
" <td>2501.000000</td>\n",
" <td>0.163400</td>\n",
" <td>0.345400</td>\n",
" <td>0.426800</td>\n",
" <td>0.201200</td>\n",
" <td>0.304000</td>\n",
" <td>0.097440</td>\n",
" <td>2.873000</td>\n",
" <td>4.885000</td>\n",
" <td>21.980000</td>\n",
" <td>542.200000</td>\n",
" <td>0.031130</td>\n",
" <td>0.135400</td>\n",
" <td>0.396000</td>\n",
" <td>0.052790</td>\n",
" <td>0.078950</td>\n",
" <td>0.029840</td>\n",
" <td>36.040000</td>\n",
" <td>49.540000</td>\n",
" <td>251.200000</td>\n",
" <td>4254.000000</td>\n",
" <td>0.222600</td>\n",
" <td>1.058000</td>\n",
" <td>1.252000</td>\n",
" <td>0.291000</td>\n",
" <td>0.663800</td>\n",
" <td>0.207500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 14.127292 19.289649 91.969033 654.889104 0.096360 \n",
"std 3.524049 4.301036 24.298981 351.914129 0.014064 \n",
"min 6.981000 9.710000 43.790000 143.500000 0.052630 \n",
"25% 11.700000 16.170000 75.170000 420.300000 0.086370 \n",
"50% 13.370000 18.840000 86.240000 551.100000 0.095870 \n",
"75% 15.780000 21.800000 104.100000 782.700000 0.105300 \n",
"max 28.110000 39.280000 188.500000 2501.000000 0.163400 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.104341 0.088799 0.048919 0.181162 \n",
"std 0.052813 0.079720 0.038803 0.027414 \n",
"min 0.019380 0.000000 0.000000 0.106000 \n",
"25% 0.064920 0.029560 0.020310 0.161900 \n",
"50% 0.092630 0.061540 0.033500 0.179200 \n",
"75% 0.130400 0.130700 0.074000 0.195700 \n",
"max 0.345400 0.426800 0.201200 0.304000 \n",
"\n",
" mean fractal dimension radius error texture error perimeter error \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.062798 0.405172 1.216853 2.866059 \n",
"std 0.007060 0.277313 0.551648 2.021855 \n",
"min 0.049960 0.111500 0.360200 0.757000 \n",
"25% 0.057700 0.232400 0.833900 1.606000 \n",
"50% 0.061540 0.324200 1.108000 2.287000 \n",
"75% 0.066120 0.478900 1.474000 3.357000 \n",
"max 0.097440 2.873000 4.885000 21.980000 \n",
"\n",
" area error smoothness error compactness error concavity error \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 40.337079 0.007041 0.025478 0.031894 \n",
"std 45.491006 0.003003 0.017908 0.030186 \n",
"min 6.802000 0.001713 0.002252 0.000000 \n",
"25% 17.850000 0.005169 0.013080 0.015090 \n",
"50% 24.530000 0.006380 0.020450 0.025890 \n",
"75% 45.190000 0.008146 0.032450 0.042050 \n",
"max 542.200000 0.031130 0.135400 0.396000 \n",
"\n",
" concave points error symmetry error fractal dimension error \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.011796 0.020542 0.003795 \n",
"std 0.006170 0.008266 0.002646 \n",
"min 0.000000 0.007882 0.000895 \n",
"25% 0.007638 0.015160 0.002248 \n",
"50% 0.010930 0.018730 0.003187 \n",
"75% 0.014710 0.023480 0.004558 \n",
"max 0.052790 0.078950 0.029840 \n",
"\n",
" worst radius worst texture worst perimeter worst area \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 16.269190 25.677223 107.261213 880.583128 \n",
"std 4.833242 6.146258 33.602542 569.356993 \n",
"min 7.930000 12.020000 50.410000 185.200000 \n",
"25% 13.010000 21.080000 84.110000 515.300000 \n",
"50% 14.970000 25.410000 97.660000 686.500000 \n",
"75% 18.790000 29.720000 125.400000 1084.000000 \n",
"max 36.040000 49.540000 251.200000 4254.000000 \n",
"\n",
" worst smoothness worst compactness worst concavity worst concave points \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.132369 0.254265 0.272188 0.114606 \n",
"std 0.022832 0.157336 0.208624 0.065732 \n",
"min 0.071170 0.027290 0.000000 0.000000 \n",
"25% 0.116600 0.147200 0.114500 0.064930 \n",
"50% 0.131300 0.211900 0.226700 0.099930 \n",
"75% 0.146000 0.339100 0.382900 0.161400 \n",
"max 0.222600 1.058000 1.252000 0.291000 \n",
"\n",
" worst symmetry worst fractal dimension \n",
"count 569.000000 569.000000 \n",
"mean 0.290076 0.083946 \n",
"std 0.061867 0.018061 \n",
"min 0.156500 0.055040 \n",
"25% 0.250400 0.071460 \n",
"50% 0.282200 0.080040 \n",
"75% 0.317900 0.092080 \n",
"max 0.663800 0.207500 "
]
},
"execution_count": 5,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#let's summarize the data\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>3- Split to Train and Test</h4>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(398, 30)\n",
"(171, 30)\n",
"(398,)\n",
"(171,)\n"
]
}
],
"source": [
"#split the data to 70% train and 30% test\n",
"x_train,x_test,y_train,y_test = \n",
"\n",
"print(x_train.shape)\n",
"print(x_test.shape)\n",
"print(y_train.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>4- Train your model: Random Forest</h4>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9773869346733668"
]
},
"execution_count": 7,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"rf_model = #define the model\n",
" #fit the model (train)\n",
" #predict on new observations\n",
"\n",
"#what is the accuracy of this model?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Let's visualize this tree! (https://medium.com/@rnbrown/creating-and-visualizing-decision-trees-with-python-f8e8fa394176)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
" \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
]
},
{
"data": {
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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 8,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#select which tree do you want to visualize\n",
"selected_tree=2\n",
"\n",
"from sklearn.externals.six import StringIO\n",
"from IPython.display import Image\n",
"from sklearn.tree import export_graphviz\n",
"import pydotplus\n",
"dot_data2 = StringIO()\n",
"export_graphviz(rf_model.estimators_[selected_tree],\n",
" out_file=dot_data2,\n",
" filled=True,\n",
" precision=2,\n",
" feature_names=x_train.columns,\n",
" rounded=True)\n",
"graph = pydotplus.graph_from_dot_data(dot_data2.getvalue())\n",
"Image(graph.create_png())"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>5- Predict!</h4>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>radius error</th>\n",
" <th>texture error</th>\n",
" <th>perimeter error</th>\n",
" <th>area error</th>\n",
" <th>smoothness error</th>\n",
" <th>compactness error</th>\n",
" <th>concavity error</th>\n",
" <th>concave points error</th>\n",
" <th>symmetry error</th>\n",
" <th>fractal dimension error</th>\n",
" <th>worst radius</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
" <th>worst symmetry</th>\n",
" <th>worst fractal dimension</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>204</td>\n",
" <td>12.47</td>\n",
" <td>18.6</td>\n",
" <td>81.09</td>\n",
" <td>481.9</td>\n",
" <td>0.09965</td>\n",
" <td>0.1058</td>\n",
" <td>0.08005</td>\n",
" <td>0.03821</td>\n",
" <td>0.1925</td>\n",
" <td>0.06373</td>\n",
" <td>0.3961</td>\n",
" <td>1.044</td>\n",
" <td>2.497</td>\n",
" <td>30.29</td>\n",
" <td>0.006953</td>\n",
" <td>0.01911</td>\n",
" <td>0.02701</td>\n",
" <td>0.01037</td>\n",
" <td>0.01782</td>\n",
" <td>0.003586</td>\n",
" <td>14.97</td>\n",
" <td>24.64</td>\n",
" <td>96.05</td>\n",
" <td>677.9</td>\n",
" <td>0.1426</td>\n",
" <td>0.2378</td>\n",
" <td>0.2671</td>\n",
" <td>0.1015</td>\n",
" <td>0.3014</td>\n",
" <td>0.0875</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"204 12.47 18.6 81.09 481.9 0.09965 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"204 0.1058 0.08005 0.03821 0.1925 \n",
"\n",
" mean fractal dimension radius error texture error perimeter error \\\n",
"204 0.06373 0.3961 1.044 2.497 \n",
"\n",
" area error smoothness error compactness error concavity error \\\n",
"204 30.29 0.006953 0.01911 0.02701 \n",
"\n",
" concave points error symmetry error fractal dimension error worst radius \\\n",
"204 0.01037 0.01782 0.003586 14.97 \n",
"\n",
" worst texture worst perimeter worst area worst smoothness \\\n",
"204 24.64 96.05 677.9 0.1426 \n",
"\n",
" worst compactness worst concavity worst concave points worst symmetry \\\n",
"204 0.2378 0.2671 0.1015 0.3014 \n",
"\n",
" worst fractal dimension \n",
"204 0.0875 "
]
},
"execution_count": 9,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#let's pull information from one patient from the test set\n",
"patient1_test=\n",
"patient1_test"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1])"
]
},
"execution_count": 10,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#what would our model predict? Malignant or Benign?\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.02191104, 0.97808896]])"
]
},
"execution_count": 11,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#can we predict the probability of a patient being malignant or benign?\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
" 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
" 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1,\n",
" 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
" 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
" 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1])"
]
},
"execution_count": 12,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#Can we predict multiple patients at once?\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.02191104, 0.97808896],\n",
" [0.98740914, 0.01259086],\n",
" [0.98740914, 0.01259086],\n",
" [0.01700678, 0.98299322],\n",
" [0.03095799, 0.96904201],\n",
" [0.99043945, 0.00956055],\n",
" [0.99043945, 0.00956055],\n",
" [0.97675077, 0.02324923],\n",
" [0.37698711, 0.62301289],\n",
" [0.01474356, 0.98525644]])"
]
},
"execution_count": 13,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#can we get the probability of each test case being malignant or benign? (display the first 10 lines)\n",
"\n",
"\n",
"#do you see how the 0 and 1 were generated in the previous command?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>6- How well did we predict?</h4>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9649122807017544"
]
},
"execution_count": 14,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#what is the accuracy of the model on the test set?\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"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>predicted benign</th>\n",
" <th>predicted malignant</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>benign</td>\n",
" <td>58</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>malignant</td>\n",
" <td>1</td>\n",
" <td>107</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" predicted benign predicted malignant\n",
"benign 58 5\n",
"malignant 1 107"
]
},
"execution_count": 15,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#let's generate a confusion matrix!\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>7- Identifying the important questions!</h4>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"#let's create a data frame that contains information about how important each question is in generating the correct prediction!\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"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>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>worst perimeter</td>\n",
" <td>0.350610</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst concavity</td>\n",
" <td>0.136664</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst concave points</td>\n",
" <td>0.120838</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean concave points</td>\n",
" <td>0.088673</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst area</td>\n",
" <td>0.055339</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean perimeter</td>\n",
" <td>0.053454</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean area</td>\n",
" <td>0.046570</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst radius</td>\n",
" <td>0.031904</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst smoothness</td>\n",
" <td>0.020717</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean concavity</td>\n",
" <td>0.019964</td>\n",
" </tr>\n",
" <tr>\n",
" <td>area error</td>\n",
" <td>0.012255</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean smoothness</td>\n",
" <td>0.007980</td>\n",
" </tr>\n",
" <tr>\n",
" <td>fractal dimension error</td>\n",
" <td>0.007435</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst fractal dimension</td>\n",
" <td>0.007163</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean radius</td>\n",
" <td>0.006920</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst texture</td>\n",
" <td>0.006043</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean texture</td>\n",
" <td>0.004531</td>\n",
" </tr>\n",
" <tr>\n",
" <td>radius error</td>\n",
" <td>0.003888</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst symmetry</td>\n",
" <td>0.003170</td>\n",
" </tr>\n",
" <tr>\n",
" <td>symmetry error</td>\n",
" <td>0.002774</td>\n",
" </tr>\n",
" <tr>\n",
" <td>concavity error</td>\n",
" <td>0.002694</td>\n",
" </tr>\n",
" <tr>\n",
" <td>worst compactness</td>\n",
" <td>0.002545</td>\n",
" </tr>\n",
" <tr>\n",
" <td>compactness error</td>\n",
" <td>0.002138</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean fractal dimension</td>\n",
" <td>0.002114</td>\n",
" </tr>\n",
" <tr>\n",
" <td>perimeter error</td>\n",
" <td>0.001663</td>\n",
" </tr>\n",
" <tr>\n",
" <td>concave points error</td>\n",
" <td>0.001098</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean compactness</td>\n",
" <td>0.000856</td>\n",
" </tr>\n",
" <tr>\n",
" <td>smoothness error</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>texture error</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean symmetry</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" importance\n",
"worst perimeter 0.350610\n",
"worst concavity 0.136664\n",
"worst concave points 0.120838\n",
"mean concave points 0.088673\n",
"worst area 0.055339\n",
"mean perimeter 0.053454\n",
"mean area 0.046570\n",
"worst radius 0.031904\n",
"worst smoothness 0.020717\n",
"mean concavity 0.019964\n",
"area error 0.012255\n",
"mean smoothness 0.007980\n",
"fractal dimension error 0.007435\n",
"worst fractal dimension 0.007163\n",
"mean radius 0.006920\n",
"worst texture 0.006043\n",
"mean texture 0.004531\n",
"radius error 0.003888\n",
"worst symmetry 0.003170\n",
"symmetry error 0.002774\n",
"concavity error 0.002694\n",
"worst compactness 0.002545\n",
"compactness error 0.002138\n",
"mean fractal dimension 0.002114\n",
"perimeter error 0.001663\n",
"concave points error 0.001098\n",
"mean compactness 0.000856\n",
"smoothness error 0.000000\n",
"texture error 0.000000\n",
"mean symmetry 0.000000"
]
},
"execution_count": 17,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#display the dataframe. Which questions do you think are important?\n",
"feature_importances"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<h4>8- Let's build another model with less features!</h4>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>worst perimeter</th>\n",
" <th>worst concave points</th>\n",
" <th>worst radius</th>\n",
" <th>mean concave points</th>\n",
" <th>worst concavity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>184.60</td>\n",
" <td>0.2654</td>\n",
" <td>25.38</td>\n",
" <td>0.14710</td>\n",
" <td>0.7119</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>158.80</td>\n",
" <td>0.1860</td>\n",
" <td>24.99</td>\n",
" <td>0.07017</td>\n",
" <td>0.2416</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>152.50</td>\n",
" <td>0.2430</td>\n",
" <td>23.57</td>\n",
" <td>0.12790</td>\n",
" <td>0.4504</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>98.87</td>\n",
" <td>0.2575</td>\n",
" <td>14.91</td>\n",
" <td>0.10520</td>\n",
" <td>0.6869</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>152.20</td>\n",
" <td>0.1625</td>\n",
" <td>22.54</td>\n",
" <td>0.10430</td>\n",
" <td>0.4000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" worst perimeter worst concave points worst radius mean concave points \\\n",
"0 184.60 0.2654 25.38 0.14710 \n",
"1 158.80 0.1860 24.99 0.07017 \n",
"2 152.50 0.2430 23.57 0.12790 \n",
"3 98.87 0.2575 14.91 0.10520 \n",
"4 152.20 0.1625 22.54 0.10430 \n",
"\n",
" worst concavity \n",
"0 0.7119 \n",
"1 0.2416 \n",
"2 0.4504 \n",
"3 0.6869 \n",
"4 0.4000 "
]
},
"execution_count": 18,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#subset the questions we are interested in\n",
"X_reduced=X[['worst perimeter','worst concave points','worst radius','mean concave points','worst concavity']] #define your features\n",
"Y=pd.Series(cancer.target) #define the target\n",
"X_reduced.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"#split into train and test\n",
"x_train,x_test,y_train,y_test = train_test_split(X_reduced,Y,test_size=0.3,random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9623115577889447"
]
},
"execution_count": 20,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#train a new model!\n",
"rf_model = RandomForestClassifier(max_depth=3,n_estimators=15) #define the model\n",
"rf_model.fit(x_train, y_train) #fit the model (train)\n",
"rf_model.score(x_train,y_train) #predict on new observations\n",
"\n",
"#what is the accuracy of this model?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
" warnings.warn(msg, category=DeprecationWarning)\n"
]
},
{
"data": {
"text/plain": [
"['cancer_classifier.pkl']"
]
},
"execution_count": 21,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#save the model!\n",
"from sklearn.externals import joblib\n",
"\n",
"joblib.dump(rf_model, \"cancer_classifier.pkl\") #save the whole model into a file to be used later\n",
"\n",
"#to load the model next time we just need to do:\n",
"#classifer = joblib.load(\"model.pkl\")\n",
"#classifer.predict(newobs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<center><h3>Congratulations! You have built your first classifier!</h3></center>\n",
"<center><h5>www.thecodinghive.com</h5></center>"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (system-wide)",
"language": "python",
"metadata": {
"cocalc": {
"description": "Python 3 programming language",
"priority": 100,
"url": "https://www.python.org/"
}
},
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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