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decisionstats/decision tree of rattle dataset

Created December 30, 2016 15:20
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decision tree of rattle dataset
{
"cells": [
{
"cell_type": "code",
"execution_count": 258,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# rattle package in R has weather dataset\n",
"#(see help at http://artax.karlin.mff.cuni.cz/r-help/library/rattle/html/weather.html)"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os as os"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/ajayohri'"
]
},
"execution_count": 261,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.hplip',\n",
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" '.thunderbird',\n",
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" 'R',\n",
" 'Desktop',\n",
" 'filename.pkl_07.npy',\n",
" '.cache',\n",
" '.webex',\n",
" 'file.R',\n",
" '.ipython',\n",
" 'unique_ids_for_list.html',\n",
" 'filename.pkl_11.npy',\n",
" '.Xauthority',\n",
" 'Dropbox',\n",
" 'examples.desktop',\n",
" 'machine learning-plot and bagged pima indians.ipynb',\n",
" 'date time.ipynb',\n",
" 'Untitled.ipynb',\n",
" '.rstudio-desktop',\n",
" 'filename.pkl_01.npy',\n",
" 'anaconda3',\n",
" '.dropbox',\n",
" 'Music',\n",
" '.pki',\n",
" 'rsconnect',\n",
" 'GoodReads.ipynb',\n",
" '.config',\n",
" 'diamsum.html',\n",
" 'filename.pkl_06.npy',\n",
" 'data inspection .ipynb',\n",
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" 'mozilla.pdf',\n",
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" '.bash_logout',\n",
" '.jupyter',\n",
" '.ecryptfs',\n",
" '.dbus',\n",
" '.local',\n",
" '.lyx',\n",
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" 'hebrew',\n",
" 'RCommanderMarkdown.Rmd',\n",
" '.bash_history',\n",
" 'SAS',\n",
" 'nbr2mp4.sh',\n",
" '.adobe',\n",
" '.Skype',\n",
" 'filename.pkl_05.npy',\n",
" '.wajig',\n",
" 'ajay ohri.odt',\n",
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" 'machine learning-rattle dataset from R.ipynb',\n",
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" '.gstreamer-0.10',\n",
" 'SVM.html',\n",
" '.Private',\n",
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" 'filename.pkl_02.npy',\n",
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" 'Public',\n",
" 'nbr2mp4.tar',\n",
" 'RcmdrMarkdown.md',\n",
" '.bashrc',\n",
" '.mozilla',\n",
" 'Pictures',\n",
" 'Data Viz Tutorial.ipynb',\n",
" 'filename.pkl_10.npy',\n",
" '.RData',\n",
" '.gconf',\n",
" 'data transformations.ipynb',\n",
" 'RcmdrMarkdown.html',\n",
" 'file.html',\n",
" 'Scikit Tutorial.ipynb',\n",
" 'Strings, Lists and Maps.ipynb',\n",
" 'filename.pkl',\n",
" 'weather.csv',\n",
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" '.gnupg',\n",
" '.nano',\n",
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" 'Templates',\n",
" '.ICEauthority',\n",
" '.ipynb_checkpoints']"
]
},
"execution_count": 262,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir()"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['weather.csv']\n"
]
}
],
"source": [
"#Finding only csv files in a directory using os and glob packages\n",
"import glob\n",
"\n",
"path = os.getcwd()\n",
"extension = 'csv'\n",
"os.chdir(path)\n",
"result = [i for i in glob.glob('*.{}'.format(extension))]\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataframe=pd.read_csv(\"weather.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Date</th>\n",
" <th>Location</th>\n",
" <th>MinTemp</th>\n",
" <th>MaxTemp</th>\n",
" <th>Rainfall</th>\n",
" <th>Evaporation</th>\n",
" <th>Sunshine</th>\n",
" <th>WindGustDir</th>\n",
" <th>WindGustSpeed</th>\n",
" <th>...</th>\n",
" <th>Humidity3pm</th>\n",
" <th>Pressure9am</th>\n",
" <th>Pressure3pm</th>\n",
" <th>Cloud9am</th>\n",
" <th>Cloud3pm</th>\n",
" <th>Temp9am</th>\n",
" <th>Temp3pm</th>\n",
" <th>RainToday</th>\n",
" <th>RISK_MM</th>\n",
" <th>RainTomorrow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2007-11-01</td>\n",
" <td>Canberra</td>\n",
" <td>8.0</td>\n",
" <td>24.3</td>\n",
" <td>0.0</td>\n",
" <td>3.4</td>\n",
" <td>6.3</td>\n",
" <td>NW</td>\n",
" <td>30.0</td>\n",
" <td>...</td>\n",
" <td>29</td>\n",
" <td>1019.7</td>\n",
" <td>1015.0</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>14.4</td>\n",
" <td>23.6</td>\n",
" <td>No</td>\n",
" <td>3.6</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2007-11-02</td>\n",
" <td>Canberra</td>\n",
" <td>14.0</td>\n",
" <td>26.9</td>\n",
" <td>3.6</td>\n",
" <td>4.4</td>\n",
" <td>9.7</td>\n",
" <td>ENE</td>\n",
" <td>39.0</td>\n",
" <td>...</td>\n",
" <td>36</td>\n",
" <td>1012.4</td>\n",
" <td>1008.4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>17.5</td>\n",
" <td>25.7</td>\n",
" <td>Yes</td>\n",
" <td>3.6</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2007-11-03</td>\n",
" <td>Canberra</td>\n",
" <td>13.7</td>\n",
" <td>23.4</td>\n",
" <td>3.6</td>\n",
" <td>5.8</td>\n",
" <td>3.3</td>\n",
" <td>NW</td>\n",
" <td>85.0</td>\n",
" <td>...</td>\n",
" <td>69</td>\n",
" <td>1009.5</td>\n",
" <td>1007.2</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>15.4</td>\n",
" <td>20.2</td>\n",
" <td>Yes</td>\n",
" <td>39.8</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2007-11-04</td>\n",
" <td>Canberra</td>\n",
" <td>13.3</td>\n",
" <td>15.5</td>\n",
" <td>39.8</td>\n",
" <td>7.2</td>\n",
" <td>9.1</td>\n",
" <td>NW</td>\n",
" <td>54.0</td>\n",
" <td>...</td>\n",
" <td>56</td>\n",
" <td>1005.5</td>\n",
" <td>1007.0</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>13.5</td>\n",
" <td>14.1</td>\n",
" <td>Yes</td>\n",
" <td>2.8</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2007-11-05</td>\n",
" <td>Canberra</td>\n",
" <td>7.6</td>\n",
" <td>16.1</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>10.6</td>\n",
" <td>SSE</td>\n",
" <td>50.0</td>\n",
" <td>...</td>\n",
" <td>49</td>\n",
" <td>1018.3</td>\n",
" <td>1018.5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>11.1</td>\n",
" <td>15.4</td>\n",
" <td>Yes</td>\n",
" <td>0.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Date Location MinTemp MaxTemp Rainfall Evaporation \\\n",
"0 1 2007-11-01 Canberra 8.0 24.3 0.0 3.4 \n",
"1 2 2007-11-02 Canberra 14.0 26.9 3.6 4.4 \n",
"2 3 2007-11-03 Canberra 13.7 23.4 3.6 5.8 \n",
"3 4 2007-11-04 Canberra 13.3 15.5 39.8 7.2 \n",
"4 5 2007-11-05 Canberra 7.6 16.1 2.8 5.6 \n",
"\n",
" Sunshine WindGustDir WindGustSpeed ... Humidity3pm Pressure9am \\\n",
"0 6.3 NW 30.0 ... 29 1019.7 \n",
"1 9.7 ENE 39.0 ... 36 1012.4 \n",
"2 3.3 NW 85.0 ... 69 1009.5 \n",
"3 9.1 NW 54.0 ... 56 1005.5 \n",
"4 10.6 SSE 50.0 ... 49 1018.3 \n",
"\n",
" Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainToday RISK_MM \\\n",
"0 1015.0 7 7 14.4 23.6 No 3.6 \n",
"1 1008.4 5 3 17.5 25.7 Yes 3.6 \n",
"2 1007.2 8 7 15.4 20.2 Yes 39.8 \n",
"3 1007.0 2 7 13.5 14.1 Yes 2.8 \n",
"4 1018.5 7 7 11.1 15.4 Yes 0.0 \n",
"\n",
" RainTomorrow \n",
"0 Yes \n",
"1 Yes \n",
"2 Yes \n",
"3 Yes \n",
"4 No \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 265,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe.head()"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 366 entries, 0 to 365\n",
"Data columns (total 25 columns):\n",
"Unnamed: 0 366 non-null int64\n",
"Date 366 non-null object\n",
"Location 366 non-null object\n",
"MinTemp 366 non-null float64\n",
"MaxTemp 366 non-null float64\n",
"Rainfall 366 non-null float64\n",
"Evaporation 366 non-null float64\n",
"Sunshine 363 non-null float64\n",
"WindGustDir 363 non-null object\n",
"WindGustSpeed 364 non-null float64\n",
"WindDir9am 335 non-null object\n",
"WindDir3pm 365 non-null object\n",
"WindSpeed9am 359 non-null float64\n",
"WindSpeed3pm 366 non-null int64\n",
"Humidity9am 366 non-null int64\n",
"Humidity3pm 366 non-null int64\n",
"Pressure9am 366 non-null float64\n",
"Pressure3pm 366 non-null float64\n",
"Cloud9am 366 non-null int64\n",
"Cloud3pm 366 non-null int64\n",
"Temp9am 366 non-null float64\n",
"Temp3pm 366 non-null float64\n",
"RainToday 366 non-null object\n",
"RISK_MM 366 non-null float64\n",
"RainTomorrow 366 non-null object\n",
"dtypes: float64(12), int64(6), object(7)\n",
"memory usage: 71.6+ KB\n"
]
}
],
"source": [
"dataframe.info()"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataframe=dataframe.drop('Unnamed: 0', 1)"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ajayohri/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
" RuntimeWarning)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MinTemp</th>\n",
" <th>MaxTemp</th>\n",
" <th>Rainfall</th>\n",
" <th>Evaporation</th>\n",
" <th>Sunshine</th>\n",
" <th>WindGustSpeed</th>\n",
" <th>WindSpeed9am</th>\n",
" <th>WindSpeed3pm</th>\n",
" <th>Humidity9am</th>\n",
" <th>Humidity3pm</th>\n",
" <th>Pressure9am</th>\n",
" <th>Pressure3pm</th>\n",
" <th>Cloud9am</th>\n",
" <th>Cloud3pm</th>\n",
" <th>Temp9am</th>\n",
" <th>Temp3pm</th>\n",
" <th>RISK_MM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>363.000000</td>\n",
" <td>364.000000</td>\n",
" <td>359.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" <td>366.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>7.265574</td>\n",
" <td>20.550273</td>\n",
" <td>1.428415</td>\n",
" <td>4.521858</td>\n",
" <td>7.909366</td>\n",
" <td>39.840659</td>\n",
" <td>9.651811</td>\n",
" <td>17.986339</td>\n",
" <td>72.035519</td>\n",
" <td>44.519126</td>\n",
" <td>1019.709016</td>\n",
" <td>1016.810383</td>\n",
" <td>3.890710</td>\n",
" <td>4.024590</td>\n",
" <td>12.358470</td>\n",
" <td>19.230874</td>\n",
" <td>1.428415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.025800</td>\n",
" <td>6.690516</td>\n",
" <td>4.225800</td>\n",
" <td>2.669383</td>\n",
" <td>3.481517</td>\n",
" <td>13.059807</td>\n",
" <td>7.951929</td>\n",
" <td>8.856997</td>\n",
" <td>13.137058</td>\n",
" <td>16.850947</td>\n",
" <td>6.686212</td>\n",
" <td>6.469422</td>\n",
" <td>2.956131</td>\n",
" <td>2.666268</td>\n",
" <td>5.630832</td>\n",
" <td>6.640346</td>\n",
" <td>4.225800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-5.300000</td>\n",
" <td>7.600000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>13.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>36.000000</td>\n",
" <td>13.000000</td>\n",
" <td>996.500000</td>\n",
" <td>996.800000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>5.100000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.300000</td>\n",
" <td>15.025000</td>\n",
" <td>0.000000</td>\n",
" <td>2.200000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.000000</td>\n",
" <td>64.000000</td>\n",
" <td>32.250000</td>\n",
" <td>1015.350000</td>\n",
" <td>1012.800000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.625000</td>\n",
" <td>14.150000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.450000</td>\n",
" <td>19.650000</td>\n",
" <td>0.000000</td>\n",
" <td>4.200000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>17.000000</td>\n",
" <td>72.000000</td>\n",
" <td>43.000000</td>\n",
" <td>1020.150000</td>\n",
" <td>1017.400000</td>\n",
" <td>3.500000</td>\n",
" <td>4.000000</td>\n",
" <td>12.550000</td>\n",
" <td>18.550000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>12.500000</td>\n",
" <td>25.500000</td>\n",
" <td>0.200000</td>\n",
" <td>6.400000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24.000000</td>\n",
" <td>81.000000</td>\n",
" <td>55.000000</td>\n",
" <td>1024.475000</td>\n",
" <td>1021.475000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>17.000000</td>\n",
" <td>24.000000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>20.900000</td>\n",
" <td>35.800000</td>\n",
" <td>39.800000</td>\n",
" <td>13.800000</td>\n",
" <td>13.600000</td>\n",
" <td>98.000000</td>\n",
" <td>41.000000</td>\n",
" <td>52.000000</td>\n",
" <td>99.000000</td>\n",
" <td>96.000000</td>\n",
" <td>1035.700000</td>\n",
" <td>1033.200000</td>\n",
" <td>8.000000</td>\n",
" <td>8.000000</td>\n",
" <td>24.700000</td>\n",
" <td>34.500000</td>\n",
" <td>39.800000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n",
"count 366.000000 366.000000 366.000000 366.000000 363.000000 \n",
"mean 7.265574 20.550273 1.428415 4.521858 7.909366 \n",
"std 6.025800 6.690516 4.225800 2.669383 3.481517 \n",
"min -5.300000 7.600000 0.000000 0.200000 0.000000 \n",
"25% 2.300000 15.025000 0.000000 2.200000 NaN \n",
"50% 7.450000 19.650000 0.000000 4.200000 NaN \n",
"75% 12.500000 25.500000 0.200000 6.400000 NaN \n",
"max 20.900000 35.800000 39.800000 13.800000 13.600000 \n",
"\n",
" WindGustSpeed WindSpeed9am WindSpeed3pm Humidity9am Humidity3pm \\\n",
"count 364.000000 359.000000 366.000000 366.000000 366.000000 \n",
"mean 39.840659 9.651811 17.986339 72.035519 44.519126 \n",
"std 13.059807 7.951929 8.856997 13.137058 16.850947 \n",
"min 13.000000 0.000000 0.000000 36.000000 13.000000 \n",
"25% NaN NaN 11.000000 64.000000 32.250000 \n",
"50% NaN NaN 17.000000 72.000000 43.000000 \n",
"75% NaN NaN 24.000000 81.000000 55.000000 \n",
"max 98.000000 41.000000 52.000000 99.000000 96.000000 \n",
"\n",
" Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n",
"count 366.000000 366.000000 366.000000 366.000000 366.000000 \n",
"mean 1019.709016 1016.810383 3.890710 4.024590 12.358470 \n",
"std 6.686212 6.469422 2.956131 2.666268 5.630832 \n",
"min 996.500000 996.800000 0.000000 0.000000 0.100000 \n",
"25% 1015.350000 1012.800000 1.000000 1.000000 7.625000 \n",
"50% 1020.150000 1017.400000 3.500000 4.000000 12.550000 \n",
"75% 1024.475000 1021.475000 7.000000 7.000000 17.000000 \n",
"max 1035.700000 1033.200000 8.000000 8.000000 24.700000 \n",
"\n",
" Temp3pm RISK_MM \n",
"count 366.000000 366.000000 \n",
"mean 19.230874 1.428415 \n",
"std 6.640346 4.225800 \n",
"min 5.100000 0.000000 \n",
"25% 14.150000 0.000000 \n",
"50% 18.550000 0.000000 \n",
"75% 24.000000 0.200000 \n",
"max 34.500000 39.800000 "
]
},
"execution_count": 268,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe.describe()"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Yes', 'No'], dtype=object)"
]
},
"execution_count": 269,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe['RainTomorrow'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['No', 'Yes'], dtype=object)"
]
},
"execution_count": 270,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe['RainToday'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Canberra'], dtype=object)"
]
},
"execution_count": 271,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe['Location'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['2007-11-01', '2007-11-02', '2007-11-03', '2007-11-04',\n",
" '2007-11-05', '2007-11-06', '2007-11-07', '2007-11-08',\n",
" '2007-11-09', '2007-11-10', '2007-11-11', '2007-11-12',\n",
" '2007-11-13', '2007-11-14', '2007-11-15', '2007-11-16',\n",
" '2007-11-17', '2007-11-18', '2007-11-19', '2007-11-20',\n",
" '2007-11-21', '2007-11-22', '2007-11-23', '2007-11-24',\n",
" '2007-11-25', '2007-11-26', '2007-11-27', '2007-11-28',\n",
" '2007-11-29', '2007-11-30', '2007-12-01', '2007-12-02',\n",
" '2007-12-03', '2007-12-04', '2007-12-05', '2007-12-06',\n",
" '2007-12-07', '2007-12-08', '2007-12-09', '2007-12-10',\n",
" '2007-12-11', '2007-12-12', '2007-12-13', '2007-12-14',\n",
" '2007-12-15', '2007-12-16', '2007-12-17', '2007-12-18',\n",
" '2007-12-19', '2007-12-20', '2007-12-21', '2007-12-22',\n",
" '2007-12-23', '2007-12-24', '2007-12-25', '2007-12-26',\n",
" '2007-12-27', '2007-12-28', '2007-12-29', '2007-12-30',\n",
" '2007-12-31', '2008-01-01', '2008-01-02', '2008-01-03',\n",
" '2008-01-04', '2008-01-05', '2008-01-06', '2008-01-07',\n",
" '2008-01-08', '2008-01-09', '2008-01-10', '2008-01-11',\n",
" '2008-01-12', '2008-01-13', '2008-01-14', '2008-01-15',\n",
" '2008-01-16', '2008-01-17', '2008-01-18', '2008-01-19',\n",
" '2008-01-20', '2008-01-21', '2008-01-22', '2008-01-23',\n",
" '2008-01-24', '2008-01-25', '2008-01-26', '2008-01-27',\n",
" '2008-01-28', '2008-01-29', '2008-01-30', '2008-01-31',\n",
" '2008-02-01', '2008-02-02', '2008-02-03', '2008-02-04',\n",
" '2008-02-05', '2008-02-06', '2008-02-07', '2008-02-08',\n",
" '2008-02-09', '2008-02-10', '2008-02-11', '2008-02-12',\n",
" '2008-02-13', '2008-02-14', '2008-02-15', '2008-02-16',\n",
" '2008-02-17', '2008-02-18', '2008-02-19', '2008-02-20',\n",
" '2008-02-21', '2008-02-22', '2008-02-23', '2008-02-24',\n",
" '2008-02-25', '2008-02-26', '2008-02-27', '2008-02-28',\n",
" '2008-02-29', '2008-03-01', '2008-03-02', '2008-03-03',\n",
" '2008-03-04', '2008-03-05', '2008-03-06', '2008-03-07',\n",
" '2008-03-08', '2008-03-09', '2008-03-10', '2008-03-11',\n",
" '2008-03-12', '2008-03-13', '2008-03-14', '2008-03-15',\n",
" '2008-03-16', '2008-03-17', '2008-03-18', '2008-03-19',\n",
" '2008-03-20', '2008-03-21', '2008-03-22', '2008-03-23',\n",
" '2008-03-24', '2008-03-25', '2008-03-26', '2008-03-27',\n",
" '2008-03-28', '2008-03-29', '2008-03-30', '2008-03-31',\n",
" '2008-04-01', '2008-04-02', '2008-04-03', '2008-04-04',\n",
" '2008-04-05', '2008-04-06', '2008-04-07', '2008-04-08',\n",
" '2008-04-09', '2008-04-10', '2008-04-11', '2008-04-12',\n",
" '2008-04-13', '2008-04-14', '2008-04-15', '2008-04-16',\n",
" '2008-04-17', '2008-04-18', '2008-04-19', '2008-04-20',\n",
" '2008-04-21', '2008-04-22', '2008-04-23', '2008-04-24',\n",
" '2008-04-25', '2008-04-26', '2008-04-27', '2008-04-28',\n",
" '2008-04-29', '2008-04-30', '2008-05-01', '2008-05-02',\n",
" '2008-05-03', '2008-05-04', '2008-05-05', '2008-05-06',\n",
" '2008-05-07', '2008-05-08', '2008-05-09', '2008-05-10',\n",
" '2008-05-11', '2008-05-12', '2008-05-13', '2008-05-14',\n",
" '2008-05-15', '2008-05-16', '2008-05-17', '2008-05-18',\n",
" '2008-05-19', '2008-05-20', '2008-05-21', '2008-05-22',\n",
" '2008-05-23', '2008-05-24', '2008-05-25', '2008-05-26',\n",
" '2008-05-27', '2008-05-28', '2008-05-29', '2008-05-30',\n",
" '2008-05-31', '2008-06-01', '2008-06-02', '2008-06-03',\n",
" '2008-06-04', '2008-06-05', '2008-06-06', '2008-06-07',\n",
" '2008-06-08', '2008-06-09', '2008-06-10', '2008-06-11',\n",
" '2008-06-12', '2008-06-13', '2008-06-14', '2008-06-15',\n",
" '2008-06-16', '2008-06-17', '2008-06-18', '2008-06-19',\n",
" '2008-06-20', '2008-06-21', '2008-06-22', '2008-06-23',\n",
" '2008-06-24', '2008-06-25', '2008-06-26', '2008-06-27',\n",
" '2008-06-28', '2008-06-29', '2008-06-30', '2008-07-01',\n",
" '2008-07-02', '2008-07-03', '2008-07-04', '2008-07-05',\n",
" '2008-07-06', '2008-07-07', '2008-07-08', '2008-07-09',\n",
" '2008-07-10', '2008-07-11', '2008-07-12', '2008-07-13',\n",
" '2008-07-14', '2008-07-15', '2008-07-16', '2008-07-17',\n",
" '2008-07-18', '2008-07-19', '2008-07-20', '2008-07-21',\n",
" '2008-07-22', '2008-07-23', '2008-07-24', '2008-07-25',\n",
" '2008-07-26', '2008-07-27', '2008-07-28', '2008-07-29',\n",
" '2008-07-30', '2008-07-31', '2008-08-01', '2008-08-02',\n",
" '2008-08-03', '2008-08-04', '2008-08-05', '2008-08-06',\n",
" '2008-08-07', '2008-08-08', '2008-08-09', '2008-08-10',\n",
" '2008-08-11', '2008-08-12', '2008-08-13', '2008-08-14',\n",
" '2008-08-15', '2008-08-16', '2008-08-17', '2008-08-18',\n",
" '2008-08-19', '2008-08-20', '2008-08-21', '2008-08-22',\n",
" '2008-08-23', '2008-08-24', '2008-08-25', '2008-08-26',\n",
" '2008-08-27', '2008-08-28', '2008-08-29', '2008-08-30',\n",
" '2008-08-31', '2008-09-01', '2008-09-02', '2008-09-03',\n",
" '2008-09-04', '2008-09-05', '2008-09-06', '2008-09-07',\n",
" '2008-09-08', '2008-09-09', '2008-09-10', '2008-09-11',\n",
" '2008-09-12', '2008-09-13', '2008-09-14', '2008-09-15',\n",
" '2008-09-16', '2008-09-17', '2008-09-18', '2008-09-19',\n",
" '2008-09-20', '2008-09-21', '2008-09-22', '2008-09-23',\n",
" '2008-09-24', '2008-09-25', '2008-09-26', '2008-09-27',\n",
" '2008-09-28', '2008-09-29', '2008-09-30', '2008-10-01',\n",
" '2008-10-02', '2008-10-03', '2008-10-04', '2008-10-05',\n",
" '2008-10-06', '2008-10-07', '2008-10-08', '2008-10-09',\n",
" '2008-10-10', '2008-10-11', '2008-10-12', '2008-10-13',\n",
" '2008-10-14', '2008-10-15', '2008-10-16', '2008-10-17',\n",
" '2008-10-18', '2008-10-19', '2008-10-20', '2008-10-21',\n",
" '2008-10-22', '2008-10-23', '2008-10-24', '2008-10-25',\n",
" '2008-10-26', '2008-10-27', '2008-10-28', '2008-10-29',\n",
" '2008-10-30', '2008-10-31'], dtype=object)"
]
},
"execution_count": 272,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe['Date'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Bagged Decision Trees for Classification\n",
"from sklearn import cross_validation\n",
"from sklearn.ensemble import BaggingClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',\n",
" 'Sunshine', 'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',\n",
" 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',\n",
" 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',\n",
" 'Temp3pm', 'RainToday', 'RISK_MM', 'RainTomorrow'],\n",
" dtype='object')"
]
},
"execution_count": 274,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe.columns"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"del dataframe['Date']"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del dataframe['Location']"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del dataframe['WindDir9am']"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del dataframe['WindSpeed3pm']"
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del dataframe['WindGustDir']\n",
"del dataframe['WindDir3pm']\n",
"del dataframe['RISK_MM']"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataframe=dataframe.replace(['Yes', 'No'], [1, 0]) #using replace to change string to numeric values\n"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataframe=dataframe.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 282,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MinTemp</th>\n",
" <th>MaxTemp</th>\n",
" <th>Rainfall</th>\n",
" <th>Evaporation</th>\n",
" <th>Sunshine</th>\n",
" <th>WindGustSpeed</th>\n",
" <th>WindSpeed9am</th>\n",
" <th>Humidity9am</th>\n",
" <th>Humidity3pm</th>\n",
" <th>Pressure9am</th>\n",
" <th>Pressure3pm</th>\n",
" <th>Cloud9am</th>\n",
" <th>Cloud3pm</th>\n",
" <th>Temp9am</th>\n",
" <th>Temp3pm</th>\n",
" <th>RainToday</th>\n",
" <th>RainTomorrow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.0</td>\n",
" <td>24.3</td>\n",
" <td>0.0</td>\n",
" <td>3.4</td>\n",
" <td>6.3</td>\n",
" <td>30.0</td>\n",
" <td>6.0</td>\n",
" <td>68</td>\n",
" <td>29</td>\n",
" <td>1019.7</td>\n",
" <td>1015.0</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>14.4</td>\n",
" <td>23.6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>14.0</td>\n",
" <td>26.9</td>\n",
" <td>3.6</td>\n",
" <td>4.4</td>\n",
" <td>9.7</td>\n",
" <td>39.0</td>\n",
" <td>4.0</td>\n",
" <td>80</td>\n",
" <td>36</td>\n",
" <td>1012.4</td>\n",
" <td>1008.4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>17.5</td>\n",
" <td>25.7</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13.7</td>\n",
" <td>23.4</td>\n",
" <td>3.6</td>\n",
" <td>5.8</td>\n",
" <td>3.3</td>\n",
" <td>85.0</td>\n",
" <td>6.0</td>\n",
" <td>82</td>\n",
" <td>69</td>\n",
" <td>1009.5</td>\n",
" <td>1007.2</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>15.4</td>\n",
" <td>20.2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13.3</td>\n",
" <td>15.5</td>\n",
" <td>39.8</td>\n",
" <td>7.2</td>\n",
" <td>9.1</td>\n",
" <td>54.0</td>\n",
" <td>30.0</td>\n",
" <td>62</td>\n",
" <td>56</td>\n",
" <td>1005.5</td>\n",
" <td>1007.0</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>13.5</td>\n",
" <td>14.1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7.6</td>\n",
" <td>16.1</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>10.6</td>\n",
" <td>50.0</td>\n",
" <td>20.0</td>\n",
" <td>68</td>\n",
" <td>49</td>\n",
" <td>1018.3</td>\n",
" <td>1018.5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>11.1</td>\n",
" <td>15.4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustSpeed \\\n",
"0 8.0 24.3 0.0 3.4 6.3 30.0 \n",
"1 14.0 26.9 3.6 4.4 9.7 39.0 \n",
"2 13.7 23.4 3.6 5.8 3.3 85.0 \n",
"3 13.3 15.5 39.8 7.2 9.1 54.0 \n",
"4 7.6 16.1 2.8 5.6 10.6 50.0 \n",
"\n",
" WindSpeed9am Humidity9am Humidity3pm Pressure9am Pressure3pm Cloud9am \\\n",
"0 6.0 68 29 1019.7 1015.0 7 \n",
"1 4.0 80 36 1012.4 1008.4 5 \n",
"2 6.0 82 69 1009.5 1007.2 8 \n",
"3 30.0 62 56 1005.5 1007.0 2 \n",
"4 20.0 68 49 1018.3 1018.5 7 \n",
"\n",
" Cloud3pm Temp9am Temp3pm RainToday RainTomorrow \n",
"0 7 14.4 23.6 0 1 \n",
"1 3 17.5 25.7 1 1 \n",
"2 7 15.4 20.2 1 1 \n",
"3 7 13.5 14.1 1 1 \n",
"4 7 11.1 15.4 1 0 "
]
},
"execution_count": 282,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe.head()"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"354"
]
},
"execution_count": 283,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataframe)"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 284,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataframe.columns)"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine',\n",
" 'WindGustSpeed', 'WindSpeed9am', 'Humidity9am', 'Humidity3pm',\n",
" 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',\n",
" 'Temp3pm', 'RainToday', 'RainTomorrow'],\n",
" dtype='object')"
]
},
"execution_count": 285,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names=dataframe.columns\n",
"names"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MinTemp</th>\n",
" <th>MaxTemp</th>\n",
" <th>Rainfall</th>\n",
" <th>Evaporation</th>\n",
" <th>Sunshine</th>\n",
" <th>WindGustSpeed</th>\n",
" <th>WindSpeed9am</th>\n",
" <th>Humidity9am</th>\n",
" <th>Humidity3pm</th>\n",
" <th>Pressure9am</th>\n",
" <th>Pressure3pm</th>\n",
" <th>Cloud9am</th>\n",
" <th>Cloud3pm</th>\n",
" <th>Temp9am</th>\n",
" <th>Temp3pm</th>\n",
" <th>RainToday</th>\n",
" <th>RainTomorrow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" <td>354.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>7.362429</td>\n",
" <td>20.601412</td>\n",
" <td>1.420904</td>\n",
" <td>4.558192</td>\n",
" <td>7.925424</td>\n",
" <td>40.011299</td>\n",
" <td>9.666667</td>\n",
" <td>71.875706</td>\n",
" <td>44.454802</td>\n",
" <td>1019.562147</td>\n",
" <td>1016.692090</td>\n",
" <td>3.920904</td>\n",
" <td>4.019774</td>\n",
" <td>12.438701</td>\n",
" <td>19.271469</td>\n",
" <td>0.180791</td>\n",
" <td>0.180791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.010927</td>\n",
" <td>6.708966</td>\n",
" <td>4.235358</td>\n",
" <td>2.667877</td>\n",
" <td>3.510039</td>\n",
" <td>13.034488</td>\n",
" <td>7.978489</td>\n",
" <td>13.161939</td>\n",
" <td>16.944316</td>\n",
" <td>6.602685</td>\n",
" <td>6.373679</td>\n",
" <td>2.962363</td>\n",
" <td>2.672312</td>\n",
" <td>5.630160</td>\n",
" <td>6.663681</td>\n",
" <td>0.385390</td>\n",
" <td>0.385390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-5.300000</td>\n",
" <td>7.600000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>13.000000</td>\n",
" <td>0.000000</td>\n",
" <td>36.000000</td>\n",
" <td>13.000000</td>\n",
" <td>996.500000</td>\n",
" <td>996.800000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>5.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.400000</td>\n",
" <td>15.100000</td>\n",
" <td>0.000000</td>\n",
" <td>2.400000</td>\n",
" <td>5.925000</td>\n",
" <td>31.000000</td>\n",
" <td>6.000000</td>\n",
" <td>64.000000</td>\n",
" <td>32.000000</td>\n",
" <td>1015.225000</td>\n",
" <td>1012.725000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.725000</td>\n",
" <td>14.300000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.500000</td>\n",
" <td>19.750000</td>\n",
" <td>0.000000</td>\n",
" <td>4.200000</td>\n",
" <td>8.650000</td>\n",
" <td>39.000000</td>\n",
" <td>7.000000</td>\n",
" <td>72.000000</td>\n",
" <td>43.000000</td>\n",
" <td>1020.000000</td>\n",
" <td>1017.200000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>12.600000</td>\n",
" <td>18.600000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>12.500000</td>\n",
" <td>25.500000</td>\n",
" <td>0.200000</td>\n",
" <td>6.400000</td>\n",
" <td>10.600000</td>\n",
" <td>46.000000</td>\n",
" <td>13.000000</td>\n",
" <td>80.000000</td>\n",
" <td>54.750000</td>\n",
" <td>1024.400000</td>\n",
" <td>1021.350000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>17.000000</td>\n",
" <td>24.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>20.900000</td>\n",
" <td>35.800000</td>\n",
" <td>39.800000</td>\n",
" <td>13.800000</td>\n",
" <td>13.600000</td>\n",
" <td>98.000000</td>\n",
" <td>41.000000</td>\n",
" <td>99.000000</td>\n",
" <td>96.000000</td>\n",
" <td>1035.700000</td>\n",
" <td>1033.200000</td>\n",
" <td>8.000000</td>\n",
" <td>8.000000</td>\n",
" <td>24.700000</td>\n",
" <td>34.500000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n",
"count 354.000000 354.000000 354.000000 354.000000 354.000000 \n",
"mean 7.362429 20.601412 1.420904 4.558192 7.925424 \n",
"std 6.010927 6.708966 4.235358 2.667877 3.510039 \n",
"min -5.300000 7.600000 0.000000 0.200000 0.000000 \n",
"25% 2.400000 15.100000 0.000000 2.400000 5.925000 \n",
"50% 7.500000 19.750000 0.000000 4.200000 8.650000 \n",
"75% 12.500000 25.500000 0.200000 6.400000 10.600000 \n",
"max 20.900000 35.800000 39.800000 13.800000 13.600000 \n",
"\n",
" WindGustSpeed WindSpeed9am Humidity9am Humidity3pm Pressure9am \\\n",
"count 354.000000 354.000000 354.000000 354.000000 354.000000 \n",
"mean 40.011299 9.666667 71.875706 44.454802 1019.562147 \n",
"std 13.034488 7.978489 13.161939 16.944316 6.602685 \n",
"min 13.000000 0.000000 36.000000 13.000000 996.500000 \n",
"25% 31.000000 6.000000 64.000000 32.000000 1015.225000 \n",
"50% 39.000000 7.000000 72.000000 43.000000 1020.000000 \n",
"75% 46.000000 13.000000 80.000000 54.750000 1024.400000 \n",
"max 98.000000 41.000000 99.000000 96.000000 1035.700000 \n",
"\n",
" Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm \\\n",
"count 354.000000 354.000000 354.000000 354.000000 354.000000 \n",
"mean 1016.692090 3.920904 4.019774 12.438701 19.271469 \n",
"std 6.373679 2.962363 2.672312 5.630160 6.663681 \n",
"min 996.800000 0.000000 0.000000 0.100000 5.100000 \n",
"25% 1012.725000 1.000000 1.000000 7.725000 14.300000 \n",
"50% 1017.200000 4.000000 4.000000 12.600000 18.600000 \n",
"75% 1021.350000 7.000000 7.000000 17.000000 24.000000 \n",
"max 1033.200000 8.000000 8.000000 24.700000 34.500000 \n",
"\n",
" RainToday RainTomorrow \n",
"count 354.000000 354.000000 \n",
"mean 0.180791 0.180791 \n",
"std 0.385390 0.385390 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 1.000000 1.000000 "
]
},
"execution_count": 286,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataframe.describe()"
]
},
{
"cell_type": "code",
"execution_count": 287,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 287,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(dataframe)"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"array = dataframe.values"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 290\n",
"1 64\n",
"Name: RainTomorrow, dtype: int64"
]
},
"execution_count": 289,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(dataframe[\"RainTomorrow\"])"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 8. , 24.3, 0. , ..., 23.6, 0. , 1. ],\n",
" [ 14. , 26.9, 3.6, ..., 25.7, 1. , 1. ],\n",
" [ 13.7, 23.4, 3.6, ..., 20.2, 1. , 1. ],\n",
" ..., \n",
" [ 12.5, 19.9, 0. , ..., 18.3, 0. , 0. ],\n",
" [ 12.5, 26.9, 0. , ..., 25.9, 0. , 0. ],\n",
" [ 12.3, 30.2, 0. , ..., 28.6, 0. , 0. ]])"
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"X = array[:,0:16]\n",
"Y = array[:,16]\n",
"num_folds = 10\n",
"num_instances = len(X)\n",
"seed = 7"
]
},
{
"cell_type": "code",
"execution_count": 292,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 292,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(X)\n"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 8. , 24.3, 0. , ..., 14.4, 23.6, 0. ],\n",
" [ 14. , 26.9, 3.6, ..., 17.5, 25.7, 1. ],\n",
" [ 13.7, 23.4, 3.6, ..., 15.4, 20.2, 1. ],\n",
" ..., \n",
" [ 12.5, 19.9, 0. , ..., 14.5, 18.3, 0. ],\n",
" [ 12.5, 26.9, 0. , ..., 15.8, 25.9, 0. ],\n",
" [ 12.3, 30.2, 0. , ..., 23.8, 28.6, 0. ]])"
]
},
"execution_count": 293,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 1., 1., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
" 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
" 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 1., 0., 0.,\n",
" 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 1., 1., 1.,\n",
" 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0.,\n",
" 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
" 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1., 1.,\n",
" 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0.,\n",
" 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,\n",
" 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0.,\n",
" 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0.,\n",
" 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
" 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.,\n",
" 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0.])"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Y[Y == \"Yes\"] = 1 An alternative way to make a NumPy arraye change values\n",
"#Y[Y == \"No\"] = 0\n",
"Y"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,\n",
" max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best')"
]
},
"execution_count": 295,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtr = tree.DecisionTreeRegressor(max_depth=3)\n",
"dtr.fit(X, Y)"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# from sklearn.metrics import roc_curve, auc"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!sudo pip install pydotplus\n",
"# http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n",
"# http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/\n",
"# http://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!pip freeze\n",
"#checking if we have the right packages"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!pip install --upgrade pip"
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!pip install pydotplus"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pydotplus as pydot\n",
"\n",
"from IPython.display import Image\n",
"\n",
"from sklearn.externals.six import StringIO"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Graphviz\n",
"#sudo add-apt-repository ppa:gviz-adm/graphviz-dev\n",
"# sudo apt-get update\n",
"# http://www.graphviz.org/Download_linux_ubuntu.php"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"\n",
"dot_data = StringIO()"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"\n",
"tree.export_graphviz(dtr, out_file=dot_data,feature_names=names[:-1])"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"\n",
"graph = pydot.graph_from_dot_data(dot_data.getvalue())"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ANBLyOgAAaDKV7jrr2LGj/K7OnTsXFBTISvbt26epqcnmLhYWFmppacXHx1tZWVVK1caN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\nu+Q7rUx+fn5AQICKigoRhYWF1en9rZtW3FcBoPV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ZDAaD6kBAnyjx/yM+qKysLKoDUUgV\nFRWpqanq6uqenp74dQiUU+JcBQAoH3huCgAAAACoZ25uHhUVRXUUAACgqOD+OgAAAAAAAABQbNCv\nAwAAAAAAAADFBv06AAAAAAAAAFBs0K8DAAAAAAAAAMUG/ToAgGJgsVhUhwAAUDm5ubnQ+AAAFAL0\n6wAA8o7L5Z49e9be3p7qQAAAKuf06dN2dnbffvstl8ulOhYAABAH+nUAALl2+fLlSZMmLV++fPz4\n8VTHAgBQOXFxce7u7mvXrh0zZkx2dja89RcAILegXwcAkFM3b96cNWuWh4eHkZFRYWEhvOsZACB7\nxsbGiYmJpaWlrq6uvr6+jo6O2dnZVAcFAABCQL8OACB3iouLGQzGlClTOjo6/vrrr9zc3HHjxiGE\nNDQ0fHx8aEDB4f9KqrOsX6irq2dnZ1P9AQOpQW9z1dzcPDEx8d69e3Z2dj4+PtOmTbty5QrV6QYA\nAP+DBiMKAADy4+XLl3v37k1JSbG2tt69e7e3tzfv0r///rumpoaq2GSDwWBs3bp16tSpVAfSj9TV\n1efPn6+trU11INJXUVFx69YtqqPod/n5+UePHlWFS+hCc7WgoGDfvn3nz593d3c/cODApEmTqAoP\nAAB4Qb8OACAXmpqaDh8+HBcXR6fTIyIiAgIC1NXVqQ6KAjQaLTMzk8FgUB0IACJlZWX5+Pio+PlD\nXl5eRETE1atX3d3djxw5gscUAAAAhWAcJgCAYh0dHdHR0ZaWlsnJyVFRUSUlJevXr1fNTh0AQFG4\nuLjgUeJNTU0TJkxgMBilpaVUBwUAUGnQrwMAUIbFYiUlJVlbW+/du3fDhg1lZWWhoaFKOTwPAKCU\n3N3d//nnn+++++7+/fujR49euXJleXk51UEBAFQU9OsAABQgCCI7O3v06NGbNm1auHDh06dPDx06\nZGBgQHVcAADQMzQazdvb+9GjR+np6devX7ezs9uwYYPS3wkMAJBD0K8DAMgafiWdj4/PuHHjHj9+\nnJiYaGJiQnVQAADQe2pqat7e3o8fP46Pjz9//ryVlVVYWFhzczPVcQEAVAj06wAAsnPr1i13d3cP\nDw9DQ0P8SjpLS0uqgwIAAOnQ1NRcv359aWnp/v37T58+bWZmFhYW1tbWRnVcAACVAP06AIAslJSU\nMBiMyZMnM5nMK1eu5ObmOjk5UR0UAABIn46OzpYtW54+fRoeHv7VV19ZWlpGR0d3dnZSHRcAQMlB\nvw4A0L+qqqo2bNgwevToBw8eZGZm5ufnu7m5UR0UAAD0Lz09vdDQ0LKysoCAgD179tjY2Bw7dqyr\nq4vquAAASgv6dQCA/vL69evo6Gh7e/tff/31+PHjDx488Pb2ptFoVMcFAAAyYmxsfOjQoYqKCn9/\n/9DQUDs7u6SkJA6HQ3VcAAAlBP06AID0dXd3JyUlWVpaHj58ODw8HL+STkNDg+q4AACAAoMHDz50\n6FBJScns2bM//fTTDz74IDs7W8Vf7A4AkDro1wEApInL5WZnZ9va2m7dunXt2rX4lXQDBw6kOi4A\nAKCYqalpYmLigwcPJkyYsGzZssmTJ+fk5FAdFABAeUC/DgAgNZcvXx43bpy/v//s2bPxK+kGDRpE\ndVAAACBH7Ozszpw5c+/ePXNz80WLFn344Yd//fUX1UEBAJQB9OsAAFJw7do1V1fX2bNn29nZFRUV\nJSYmDhs2jOqgAABATo0ZMyYrKys/P3/gwIFubm4eHh6FhYVUBwUAUGzQrwMA9MnDhw8ZDIaLi4u2\ntvY///yTlZVlbW1NdVAAAKAApkyZcvny5b///rurq2vSpEmenp737t2jOigAgKKCfh0AoJcqKys3\nbNjg6OhYUVHxxx9/5Obmjh8/nuqgAABAwbi4uPz3v/+9dOlSVVXV+PHjGQzG06dPqQ4KAKB4oF8H\nAOix+vr6sLAwW1vbq1evfvfddwUFBTNnzqQ6KAAAUGDu7u6FhYXffffdvXv3HBwcNmzYUFVVRXVQ\nAABFAv06AEAPMJnM6OhoS0vLtLS0uLi4hw8fwivpAABAKmg0mre39+PHj9PT0y9fvmxhYbFhw4ba\n2lqq4wIAKAbo1wEAJML3SrrS0lJ4JR0AAEidmpoa7t3Fx8fn5ORYWlqGhYW1tLRQHRcAQN5Bvw4A\n8A74lXR2dnZbt25ds2YNvJIOAAD6m5aW1vr1658+fbpz5078m9quXbtev35NdVwAAPkF/ToAgDiX\nL192cnLy8/Pz8PAoLS2FV9IBAIDM6OjohIaGPn/+/D//+U9sbKylpWV0dPSbN2+ojgsAII+gXwcA\nEO769eszZsyYPXu2ra0tfiXd8OHDqQ4KAABUjp6eXmhoaFlZ2dq1a3fv3m1jY5OUlMRms6mOCwAg\nX6BfBwDgV1RUxGAwPvzwQy0trZs3b2ZlZdnY2FAdFAAAqDRjY+NDhw6VlJQsWbJk8+bN1tbWSUlJ\nHA6H6rgAAPIC+nUAgP/v+fPn+JV05eXlubm5ubm5EydOpDooAAAA/2fkyJHHjh0rLi6ePXv2J598\n4ujomJ2dTRAE1XEBAKgH/ToAAEIINTQ0hIWF2djYXLp0KSUl5ebNm+7u7lQHBQAAQAgzM7PExMQH\nDx44ODj4+PhMnTo1JyeH6qAAABSDfh0Aqo58JV1KSsru3buLi4tXrlwJr6QDAAA5Z29vn5WVde/e\nPVNT00WLFrm4uFy9epXqoAAAlIF+HQCqC7+SzsrKav/+/Rs3bsQvMNDS0qI6LgAAAJIaO3ZsVlbW\n9evXtbS0PvroIw8Pj9u3b1MdFACAAtCvA0AV4VfS2dvbBwUFLV68uKys7NChQ++99x7VcQEAAOiN\nqVOnXrlyJTc3t7W1deLEiQwGo7i4mOqgAAAyBf06AFTO5cuXJ0yYsGzZsgkTJhQXFycmJg4ePJjq\noAAAAPSVu7v7zZs3L126VFpa6uDgwGAwysrKqA4KACAj0K8DQIXcuHEDj9Kh0+l3797NysoyNzen\nOigAAADS5O7uXlhY+N133929e9fe3n7Dhg3V1dVUBwUA6HfQrwNAJTx69IjBYEydOpXNZv/999+5\nubljx46lOigAAAD9Qk1Nzdvbu6ioKDk5OTc319raesuWLbW1tVTHBQDoR9CvA0DJvXjxYsOGDR98\n8MHjx4+zsrLy8vJcXFyoDgoAAEC/09TUXLly5ZMnT44ePZqVlWVlZRUWFtba2kp1XACAfgH9OgCU\nVmNjI34l3e+//37ixIl79+55e3tTHRQAAACZ0tLSWr9+/dOnTyMiIpKSkiwtLaOjozs6OqiOCwAg\nZTSCIKiOAQAgZe3t7QkJCQcPHtTU1Pz888+Dg4MHDBhAdVBAuNOnT58+fZqcvH//vpmZmYGBAZ40\nMzNLTU2lJjIAeKxataqyshL/3draWllZ+cEHH5BLV69evXr1amoiAz3x+vXrEydOHDhwYMCAAZ99\n9tmWLVu0tbWpDgoAIB3QrwNAqbBYrFOnTkVFRTGZzE8//XTHjh36+vpUBwXEiYqK2rNnj6illpaW\nT58+lWU8AAhlbW0tJhUjIyN3794ty3hAXzQ0NHzxxRfHjh0bMmRIeHj42rVrNTQ0qA4KANBXMA4T\nACXB+0q6RYsW4VfSQadO/vn6+opapKmpCddAgJxYuXKlpqZHCgIkAAAgAElEQVSmqKU+Pj6yDAb0\nEZ1OP3ToUGlpqZeXV1BQ0NixY8+cOcPlcqmOCwDQJ9CvA0AxlJWV7d27V9QF9suXL0+cOHHZsmXj\nx49/8uRJYmLikCFDZBwh6B07O7sxY8bQaDTBRSwWC06XgZzw8/Njs9mC82k02tixYx0cHGQfEuij\nkSNHHjt2rLi4ePr06WvXrnV0dMzOzhZV+P79+7m5ubIMDwDQU9CvA0ABVFdXf/TRR5GRkT/88APf\nooKCgpkzZ3p4eBgbG9+5cycrK+v999+nJEjQaytXrlRXV+ebSaPRnJycrK2tKQkJAD6Wlpbjxo0T\n/AFCQ0Nj5cqVlIQEpMLc3DwxMfH+/fv29vY+Pj5Tp079448/+MpwuVxvb+958+b98ssvlAQJAJAE\n9OsAkHfNzc2zZs2qra1VU1MLDQ0lfzJ//PgxfiXdmzdvrl69mpuby/sYA6BA/P39ORwO30x1dfVV\nq1ZREg8AQgn9AYLNZi9btoySeIAUOTg4ZGVl3bhxg06nu7u7u7i4/P333+TSjIyM0tJS3LsT7PUB\nAOQE9OsAkGudnZ3z588vKytjsVhcLreioiIlJeXly5cbNmwYO3bso0ePMjMzr1+/Pn36dKojBb03\nfPjwqVOnqqn9T4PM4XCWLl1KVUgACPLx8eG7BUtNTW3q1KkjR46kKiQgXc7Ozjk5OdeuXdPU1Jw+\nfbqHh8edO3dYLFZ4eDiNRiMIgsPhLFiwIC8vj+pIAQBCQL8OAPnFYrGWLFnyzz//sFgsPIcgiLCw\nMGtr68uXL6empt6/fx9eSaccVqxYwTvCTU1Nbfr06SNGjKAwJAD4DBs2zMXFhfeSHY1Gg0GYymfa\ntGl//vnnhQsXGhsbJ06cOGfOnJcvX+IuPZfLZbPZc+fOvXPnDtVhAgD4Qb8OADlFEERgYODly5d5\nn1VAEMTr168XLlz4+PFjf39/vis8QHExGAzefh2NRluxYgWF8QAglGBawlVlZTV//vzCwsK0tLTb\nt2/zPrKLw+F0dXW5u7s/efKEwvAAAILgpBAAOfXZZ5+lpaUJ3nbFZrMvXrzIZDIpiQr0EyMjI3d3\nd/IVUjQabcmSJdSGBICgpUuXkj8nqaur4yc2URsS6D80Gq2mpobJZPI9ipnNZr9+/drNzY18VT0A\nQB5Avw4AebRnz57Y2FhRbxPq6uo6fPiwjEMC/W358uX4f1xDQ2PevHlGRkZURwQAv0GDBs2dOxf/\nAEEQBFxVVm7t7e379u0T/HkRIcRisRobGz/66KOamhrZBwYAEAr6dQDInZMnT0ZFRYl6VR1CiMVi\nHT16tKqqSpZRgf7m5eWlpaWFEOJwOMuXL6c6HACEIx/fqqmpuWjRIqrDAf0oNja2tbVV1FIWi1VV\nVeXu7t7S0iLLqAAAokC/DgD5kpWV9emnnwpdpK6ujs/7EUIcDufhw4cyjAv0O11dXXyWrK2tvXDh\nQqrDAUA4T09PbW1thNDixYv19PSoDgf0o8zMTA6Ho66uPmDAAKG3c7NYrJKSkrlz57a3t8s+PAAA\nHw2qAwAA/H+5ubn+/v74Sp2GhgZ+qDRCSFdX18rKytHR0c7OztbW1s7OzsrKiuzjAaXh7++flZX1\n73//W0dHh+pYABBOR0fnX//6V3p6OlxVVnr//e9/79+///Tp09LS0tLS0kePHpWXl7958wYhpKGh\noaGh0dXVxWKxCgoKFi1a9Ntvv8G3EgDUookZ6wUo9/fff8+cOZP3cYhAueEnIg4fPtzBwWHMmDG2\ntra4F2diYkJ1aH21bdu2o0ePUh0FUB4jR4588eJFv+4CWmDQCzLITEpAGw56R1lrhHyC63Vy7dWr\nV2w2Oysri+pAgIwwGIy0tDR/f3+qA5G+ly9fTpkyZdu2bVQHApRBfn6+DE4xoQUGPSWbzKSEmDac\nIIiWlpZBgwbxvqwFAKTUNUI+Qb9OAcCLp1WKpqYm1SH0l1GjRkEyA6mQ5UgTSFogOeUeAwVtOOgp\n5a4RcgiemwIAAAAAAAAAig36dQAAAAAAAACg2KBfBwAAAAAAAACKDfp1AAAAAAAAAKDYoF8HAAAA\nAAAAAIoN+nUA9BJBECUlJe8s9uzZs2PHjh0+fLi0tFQGUQEghySsLEAqWlpaqA5BCUEOKyioDkCl\nQL9O4R05cgS/NEZdXX327NkLFy5csGDBrFmzTE1NaTTa8+fPqQ5QUpMnTw4JCaE6ineIj4+nvaWm\nphYXFyemcFtb26ZNmzw8PD744IOQkBBra2uZxamg8vLy5syZgz9bDw8PNzc3V1fXTZs21dbWUh3a\nOzQ3N2/cuDEyMjI4OHjVqlXV1dVUR9RjVVVVKSkpDAZj6tSpvPMJgvjmm2+8vb3Dw8MDAwMzMjIk\nWQtJXFkIgkhOTh43bpyenp6jo2NKSopiPRf76tWrixcvxoe5efNm/PNNaWlpUFAQnrlo0aKrV69K\nd6dCW8s3b97s379/6tSpxsbG4ku+k6Lns/j4i4qKFi9ebGxsTKfTly1bJuboetTgA6Sk1QH1JGfk\nlqhDmDFjBk1AWVmZ4BbEfxcAeUEAOZaZmSnJ/1FVVRVCyMrKincmh8NZsGDB06dP+y26vnr+/Dnv\npI+PT0REBFXBSKK7u3vq1KkH3zpy5Ehtba2owrW1tU5OTtbW1nV1dZLvAiGUmZkpjWDljre3t7e3\n9zuLvXz5kjeZa2pqZs6caWBgcOvWrX4OsPc6OjpsbGz279+PJ7/++uuhQ4e+fPmS2qh6obKyEiFk\na2vLO3P37t1mZmZNTU0EQTQ1NZmZmcXGxr5zLckrS2hoqL+/f0JCwubNm7W1tRFCcXFx7wxVwrax\nj3rUAtPpdC6XS87kcrl0Oh0hVFVVJfXARLWWHR0dhoaGvDHzleRrdYVS9HwWH39RUZGXl9ePP/54\n+/bt5cuXI4RmzpwpdDs9avBJsslMSkjYhitZdSB6kjNyS9QhPHz40NHR8ciRI6fe+vjjj8eOHSt0\nI+/8LhBKiWuEfILPWq5JWB+4XK7geRVBEHl5eTU1Nf0TWl89e/bMxcVF9vu9fv36jh07erduamrq\n8ePHJSnJ5XLnzZunpqaWn5/fo11Av44gCL5kfvDgAUJoyZIl/RZaXx06dAghVFxcjCe7u7sNDQ0D\nAgJkHEZfcpvE9+FXVFRoaGgcOHCAnLNv3z4dHZ2GhgYxaxESV5bnz5/7+fmRkxcvXkQIWVpavnNF\nuerXEcI+AYIgbG1tZX9CI2anEra6ip7P4uOPjY1tb28nFxkYGOjq6grdjuQNPi8lPovtdRuOKWh1\nIHqSM/2qLy28qEM4e/ZsfX09b8nVq1fv2bNHcAsSfhcIUuIaIZ9gHKYyoNFogjMfP37s5OQ0dOhQ\n2cfzTi9fvly4cGF9fb3M9sjlcs+fPz99+nQXF5e2trbebSE6OjosLMzDwyMyMrK8vFxM4fPnz//2\n229z5syZMmVKb0MG/8fMzAwhhH8Dlk94WJGpqSme1NTUnDBhQnZ2NiGT8YR9z20x0tPT2Wz2rFmz\nyDkzZ87s6OhITk4WH5KElaWysjImJoacnD17Np1Or6urk0rwgJfkra6i57P4+Lds2aKjo0MWZrPZ\nAQEBQsOQvMEHCqdHJyES5kw/kUoLL+oQli1bhq+jYl1dXT/99NPSpUsFt9C77wIge9CvU0JcLre2\ntjYoKAg3AdnZ2UZGRjQaLSIiAhc4ceKEmppaUlISQqikpGTp0qWhoaErVqxwdXW9f/8+QRD5+fmf\nffaZubl5TU3Nv//9byMjozFjxvzwww949dbW1pCQkLCwsG3bts2ePXvbtm3Nzc0cDuevv/4KDg42\nNzevqqqaMWOGqalpc3Oz4PYRQqdPn3706FFNTc3HH3+MEOJwOFlZWatWrZo+fbqYXRAE8csvv6xf\nv37kyJHNzc2rVq0yNjYeM2bMP//8I+bT6O7uTk1NHTt27NKlSx0cHIqLi+Pj4xFCjY2NT0TA48r4\ntLW1zZkzZ/Lkyfn5+Xv37rWzs9uzZ4+onaampiKETE1Np0+frqenN378+PPnz/f4PxIghBC6efMm\nQmjKlClCE6yzszM6OjogIGDixInu7u744h5C6NatW5MnT/7000937typoaHx+vVroTMTExPx7QQI\noba2tpiYGDwpKp+F7g7f/tfU1ETGTKfT29raampqkLAqhhBqb29PS0vz9fWdNm1afn6+k5OTmZlZ\nXl5ecXGxl5cXnU63s7MTn9hIerktRl5eHkJo5MiR5JxRo0YhhO7duydmLckri4uLi4mJCd9Bubq6\n9ihIhSAq01APk0Gwtezo6Ni2bdv69esjIiK2b9/e3t4utCRfq5uWlqajo0Oj0Q4dOsRmsxFC6enp\nWlpaqampip7P4uMncbncyMjI2NjY2NhYwY30qMEHPaVA1YE3bN6cEX+mJFc1QtQhCC79/fffR44c\naW9vL7iod98FgAJUXiwE79KjUUCCXr16hZfiu71//fVXPFlZWenr64v/trKysrCwIN5emh89ejSb\nzc7JycE3umzatOnq1avp6el6enoIoby8vLa2Nmtr66ioKLx6bW2ttbX1+++/X1NTc+3atYEDByKE\nDhw4kJubGxAQ8Pr1a8HtkwHzjtPgvUVH1C6amppevHihq6uLENq3b19FRcW3336LEHJ2dhb6mbS1\ntX3xxRcjRowwMDAICwsjPw3syJEjoirFhx9+KOajbmlp2bdvn7q6OkIoOTlZaBl8iemLL76orq7O\nz8/HTWFBQYGYzZIfC4zDRAhZW1uz2eyGhoaffvrJ1NT0vffeu3v3rtAECwwMfPz4MV7Rw8NjyJAh\nra2tBEFYW1sbGhriGzwYDAa+MUboTAsLC95ahiffvHkj+e58fX0RQqmpqeRGVqxYgRDCN28IrQIc\nDgc/UUBfX//8+fNFRUUIITMzs8OHD7e0tNy+fRshNGPGDFEfUT/lNiFQMR0dHRFCHR0d5Bx8njRl\nyhQxa5EkqSy88vLytLW1CwsL31lSDsdhvvNLVmimET1PBt7WksViOTs7BwYG4qx++vQp/rQFSxIC\n/03h4eEIoYcPH5KFvby8CIJQ9HwWHz/2448/4p8PzM3Nv/76a947wfj0NIeVeNRZj9pwpakOJL6c\nYbFYYs6U5KpGiDoEwbT38/PbtWuX0HUl/C4QpMQ1Qj7BZy3Xend3B4fDefXqlYuLC9kWdHV1jRo1\nytPTE09GRETcvn0b/x0TE5ORkYHXsrCw0NDQwPPxwxuZTCaePHr0KELIx8dnx44dCKHq6mpy1/gH\nrZCQEIIgbGxsEEKNjY3kUlHb52tSeW8RlGQX5FpDhgzR0tIS/EB++uknAwOD4cOHHz58GJ/oS9dX\nX32FEHJychK6dMCAASYmJuQk7n/6+/u/c7MI+nU85wQDBgwYNWpUQEAAeasMX4LduHFD8IstJyeH\nIAg8tiQ2NpbD4Tx48ADngNCZfDdg8E5KuLuCggIajTZs2LC8vLyWlpbvv//exMREXV2dxWIRoqsA\n322xw4cP501sOp1uYGAg9PPp19zmq5j4JKCzs5Oc09HRgRAaP368mLX4iK8sJBaLNX36dPxZvZMc\n9uveeUORmMkeJQNvYfz7fVFREbkUN91CN8sXZENDg56eHnnj2YEDB3DdUfR8Fh8/1tTUVFRUFB8f\nj3+7OXXqlPhtSpjDhFKfxUr3/jpFqQ4koTkj6kypp0fBq/9aePFp397erqenx/vp8ZLwu0CQEtcI\n+QTjMJWQmpqaiYnJ1q1bNTU18RwtLa0tW7acP3++rKysu7u7uLjYyckJL9q2bZunp+fx48f379/f\n1dWFRyDgjSCE8MUxhNCiRYsQQqWlpdeuXUMIvffee+Tu8KiG69evo7d3+hkZGZFLRW2fD+8tgpLs\nglzL0NCwu7tbcIN1dXWtra3W1tbjxo3j3ZS0BAYGamtri3qdkYmJCfnhI4Tc3NwQQsXFxVIPQ1nh\n78I3b948f/48OTkZ96+QQILdunXLwcGBr1FbuHAhQujkyZN6enrBwcHOzs5MJlNfX1/UTDEk3J2z\ns/OFCxeGDRs2Z86cGTNmdHR0cLlcNzc3DQ0NJLoK8N0Wy5ulNBrN2Ni4tbVVaFT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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 306,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"Image(graph.create_png())"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"kfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed)\n",
"cart = DecisionTreeClassifier()\n",
"num_trees = 100\n",
"model = BaggingClassifier(base_estimator=cart, n_estimators=num_trees, random_state=seed)\n"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'),\n",
" bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
" max_samples=1.0, n_estimators=100, n_jobs=1, oob_score=False,\n",
" random_state=7, verbose=0, warm_start=False)"
]
},
"execution_count": 308,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"sklearn.cross_validation.KFold(n=354, n_folds=10, shuffle=False, random_state=7)"
]
},
"execution_count": 309,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kfold"
]
},
{
"cell_type": "code",
"execution_count": 310,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.850873015873\n"
]
}
],
"source": [
"results = cross_validation.cross_val_score(model, X, Y, cv=kfold)\n",
"print(results.mean())"
]
},
{
"cell_type": "code",
"execution_count": 311,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.75 , 0.86111111, 0.69444444, 0.88888889, 0.88571429,\n",
" 0.82857143, 0.91428571, 0.85714286, 0.94285714, 0.88571429])"
]
},
"execution_count": 311,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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