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pandasからnparrayに直す手習い
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pandas_to_sklean.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# いつもの呪文\n",
"import pandas as pd\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"from IPython.display import display\n",
"font = {\"family\": \"TakaoGothic\"}\n",
"matplotlib.rc('font', **font)\n",
"%matplotlib inline\n",
"\n",
"# 実効的に無視して問題ないwarningを非表示に\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import svm, datasets, model_selection, metrics"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_pandas = sns.load_dataset('iris')\n",
"iris_sklearn = datasets.load_iris()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandasとsklearnにおけるIRISの違い"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(iris_pandas)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_pandas.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sklearn.datasets.base.Bunch"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(iris_sklearn)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n :Number of Instances: 150 (50 in each of three classes)\\n :Number of Attributes: 4 numeric, predictive attributes and the class\\n :Attribute Information:\\n - sepal length in cm\\n - sepal width in cm\\n - petal length in cm\\n - petal width in cm\\n - class:\\n - Iris-Setosa\\n - Iris-Versicolour\\n - Iris-Virginica\\n :Summary Statistics:\\n\\n ============== ==== ==== ======= ===== ====================\\n Min Max Mean SD Class Correlation\\n ============== ==== ==== ======= ===== ====================\\n sepal length: 4.3 7.9 5.84 0.83 0.7826\\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\\n ============== ==== ==== ======= ===== ====================\\n\\n :Missing Attribute Values: None\\n :Class Distribution: 33.3% for each of 3 classes.\\n :Creator: R.A. Fisher\\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature. Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day. (See Duda & Hart, for example.) The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant. One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n Mathematical Statistics\" (John Wiley, NY, 1950).\\n - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\\n - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n Structure and Classification Rule for Recognition in Partially Exposed\\n Environments\". IEEE Transactions on Pattern Analysis and Machine\\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\\n on Information Theory, May 1972, 431-433.\\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\\n conceptual clustering system finds 3 classes in the data.\\n - Many, many more ...\\n',\n",
" 'data': array([[ 5.1, 3.5, 1.4, 0.2],\n",
" [ 4.9, 3. , 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.3, 0.2],\n",
" [ 4.6, 3.1, 1.5, 0.2],\n",
" [ 5. , 3.6, 1.4, 0.2],\n",
" [ 5.4, 3.9, 1.7, 0.4],\n",
" [ 4.6, 3.4, 1.4, 0.3],\n",
" [ 5. , 3.4, 1.5, 0.2],\n",
" [ 4.4, 2.9, 1.4, 0.2],\n",
" [ 4.9, 3.1, 1.5, 0.1],\n",
" [ 5.4, 3.7, 1.5, 0.2],\n",
" [ 4.8, 3.4, 1.6, 0.2],\n",
" [ 4.8, 3. , 1.4, 0.1],\n",
" [ 4.3, 3. , 1.1, 0.1],\n",
" [ 5.8, 4. , 1.2, 0.2],\n",
" [ 5.7, 4.4, 1.5, 0.4],\n",
" [ 5.4, 3.9, 1.3, 0.4],\n",
" [ 5.1, 3.5, 1.4, 0.3],\n",
" [ 5.7, 3.8, 1.7, 0.3],\n",
" [ 5.1, 3.8, 1.5, 0.3],\n",
" [ 5.4, 3.4, 1.7, 0.2],\n",
" [ 5.1, 3.7, 1.5, 0.4],\n",
" [ 4.6, 3.6, 1. , 0.2],\n",
" [ 5.1, 3.3, 1.7, 0.5],\n",
" [ 4.8, 3.4, 1.9, 0.2],\n",
" [ 5. , 3. , 1.6, 0.2],\n",
" [ 5. , 3.4, 1.6, 0.4],\n",
" [ 5.2, 3.5, 1.5, 0.2],\n",
" [ 5.2, 3.4, 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.6, 0.2],\n",
" [ 4.8, 3.1, 1.6, 0.2],\n",
" [ 5.4, 3.4, 1.5, 0.4],\n",
" [ 5.2, 4.1, 1.5, 0.1],\n",
" [ 5.5, 4.2, 1.4, 0.2],\n",
" [ 4.9, 3.1, 1.5, 0.1],\n",
" [ 5. , 3.2, 1.2, 0.2],\n",
" [ 5.5, 3.5, 1.3, 0.2],\n",
" [ 4.9, 3.1, 1.5, 0.1],\n",
" [ 4.4, 3. , 1.3, 0.2],\n",
" [ 5.1, 3.4, 1.5, 0.2],\n",
" [ 5. , 3.5, 1.3, 0.3],\n",
" [ 4.5, 2.3, 1.3, 0.3],\n",
" [ 4.4, 3.2, 1.3, 0.2],\n",
" [ 5. , 3.5, 1.6, 0.6],\n",
" [ 5.1, 3.8, 1.9, 0.4],\n",
" [ 4.8, 3. , 1.4, 0.3],\n",
" [ 5.1, 3.8, 1.6, 0.2],\n",
" [ 4.6, 3.2, 1.4, 0.2],\n",
" [ 5.3, 3.7, 1.5, 0.2],\n",
" [ 5. , 3.3, 1.4, 0.2],\n",
" [ 7. , 3.2, 4.7, 1.4],\n",
" [ 6.4, 3.2, 4.5, 1.5],\n",
" [ 6.9, 3.1, 4.9, 1.5],\n",
" [ 5.5, 2.3, 4. , 1.3],\n",
" [ 6.5, 2.8, 4.6, 1.5],\n",
" [ 5.7, 2.8, 4.5, 1.3],\n",
" [ 6.3, 3.3, 4.7, 1.6],\n",
" [ 4.9, 2.4, 3.3, 1. ],\n",
" [ 6.6, 2.9, 4.6, 1.3],\n",
" [ 5.2, 2.7, 3.9, 1.4],\n",
" [ 5. , 2. , 3.5, 1. ],\n",
" [ 5.9, 3. , 4.2, 1.5],\n",
" [ 6. , 2.2, 4. , 1. ],\n",
" [ 6.1, 2.9, 4.7, 1.4],\n",
" [ 5.6, 2.9, 3.6, 1.3],\n",
" [ 6.7, 3.1, 4.4, 1.4],\n",
" [ 5.6, 3. , 4.5, 1.5],\n",
" [ 5.8, 2.7, 4.1, 1. ],\n",
" [ 6.2, 2.2, 4.5, 1.5],\n",
" [ 5.6, 2.5, 3.9, 1.1],\n",
" [ 5.9, 3.2, 4.8, 1.8],\n",
" [ 6.1, 2.8, 4. , 1.3],\n",
" [ 6.3, 2.5, 4.9, 1.5],\n",
" [ 6.1, 2.8, 4.7, 1.2],\n",
" [ 6.4, 2.9, 4.3, 1.3],\n",
" [ 6.6, 3. , 4.4, 1.4],\n",
" [ 6.8, 2.8, 4.8, 1.4],\n",
" [ 6.7, 3. , 5. , 1.7],\n",
" [ 6. , 2.9, 4.5, 1.5],\n",
" [ 5.7, 2.6, 3.5, 1. ],\n",
" [ 5.5, 2.4, 3.8, 1.1],\n",
" [ 5.5, 2.4, 3.7, 1. ],\n",
" [ 5.8, 2.7, 3.9, 1.2],\n",
" [ 6. , 2.7, 5.1, 1.6],\n",
" [ 5.4, 3. , 4.5, 1.5],\n",
" [ 6. , 3.4, 4.5, 1.6],\n",
" [ 6.7, 3.1, 4.7, 1.5],\n",
" [ 6.3, 2.3, 4.4, 1.3],\n",
" [ 5.6, 3. , 4.1, 1.3],\n",
" [ 5.5, 2.5, 4. , 1.3],\n",
" [ 5.5, 2.6, 4.4, 1.2],\n",
" [ 6.1, 3. , 4.6, 1.4],\n",
" [ 5.8, 2.6, 4. , 1.2],\n",
" [ 5. , 2.3, 3.3, 1. ],\n",
" [ 5.6, 2.7, 4.2, 1.3],\n",
" [ 5.7, 3. , 4.2, 1.2],\n",
" [ 5.7, 2.9, 4.2, 1.3],\n",
" [ 6.2, 2.9, 4.3, 1.3],\n",
" [ 5.1, 2.5, 3. , 1.1],\n",
" [ 5.7, 2.8, 4.1, 1.3],\n",
" [ 6.3, 3.3, 6. , 2.5],\n",
" [ 5.8, 2.7, 5.1, 1.9],\n",
" [ 7.1, 3. , 5.9, 2.1],\n",
" [ 6.3, 2.9, 5.6, 1.8],\n",
" [ 6.5, 3. , 5.8, 2.2],\n",
" [ 7.6, 3. , 6.6, 2.1],\n",
" [ 4.9, 2.5, 4.5, 1.7],\n",
" [ 7.3, 2.9, 6.3, 1.8],\n",
" [ 6.7, 2.5, 5.8, 1.8],\n",
" [ 7.2, 3.6, 6.1, 2.5],\n",
" [ 6.5, 3.2, 5.1, 2. ],\n",
" [ 6.4, 2.7, 5.3, 1.9],\n",
" [ 6.8, 3. , 5.5, 2.1],\n",
" [ 5.7, 2.5, 5. , 2. ],\n",
" [ 5.8, 2.8, 5.1, 2.4],\n",
" [ 6.4, 3.2, 5.3, 2.3],\n",
" [ 6.5, 3. , 5.5, 1.8],\n",
" [ 7.7, 3.8, 6.7, 2.2],\n",
" [ 7.7, 2.6, 6.9, 2.3],\n",
" [ 6. , 2.2, 5. , 1.5],\n",
" [ 6.9, 3.2, 5.7, 2.3],\n",
" [ 5.6, 2.8, 4.9, 2. ],\n",
" [ 7.7, 2.8, 6.7, 2. ],\n",
" [ 6.3, 2.7, 4.9, 1.8],\n",
" [ 6.7, 3.3, 5.7, 2.1],\n",
" [ 7.2, 3.2, 6. , 1.8],\n",
" [ 6.2, 2.8, 4.8, 1.8],\n",
" [ 6.1, 3. , 4.9, 1.8],\n",
" [ 6.4, 2.8, 5.6, 2.1],\n",
" [ 7.2, 3. , 5.8, 1.6],\n",
" [ 7.4, 2.8, 6.1, 1.9],\n",
" [ 7.9, 3.8, 6.4, 2. ],\n",
" [ 6.4, 2.8, 5.6, 2.2],\n",
" [ 6.3, 2.8, 5.1, 1.5],\n",
" [ 6.1, 2.6, 5.6, 1.4],\n",
" [ 7.7, 3. , 6.1, 2.3],\n",
" [ 6.3, 3.4, 5.6, 2.4],\n",
" [ 6.4, 3.1, 5.5, 1.8],\n",
" [ 6. , 3. , 4.8, 1.8],\n",
" [ 6.9, 3.1, 5.4, 2.1],\n",
" [ 6.7, 3.1, 5.6, 2.4],\n",
" [ 6.9, 3.1, 5.1, 2.3],\n",
" [ 5.8, 2.7, 5.1, 1.9],\n",
" [ 6.8, 3.2, 5.9, 2.3],\n",
" [ 6.7, 3.3, 5.7, 2.5],\n",
" [ 6.7, 3. , 5.2, 2.3],\n",
" [ 6.3, 2.5, 5. , 1.9],\n",
" [ 6.5, 3. , 5.2, 2. ],\n",
" [ 6.2, 3.4, 5.4, 2.3],\n",
" [ 5.9, 3. , 5.1, 1.8]]),\n",
" 'feature_names': ['sepal length (cm)',\n",
" 'sepal width (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)'],\n",
" 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
" 'target_names': array(['setosa', 'versicolor', 'virginica'], \n",
" dtype='<U10')}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_sklearn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## sklearnにデータを食わす際はnp.arrayになってなければならない"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.1, 3.5, 1.4, 0.2],\n",
" [ 4.9, 3. , 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.3, 0.2],\n",
" [ 4.6, 3.1, 1.5, 0.2],\n",
" [ 5. , 3.6, 1.4, 0.2]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_sklearn.data[0:5,:] #最初の５行だけ表示"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_sklearn.target"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = svm.SVC()\n",
"clf.fit(iris_sklearn.data, iris_sklearn.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pandasからnp.arrayに変換"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.1, 3.5, 1.4, 0.2],\n",
" [ 4.9, 3. , 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.3, 0.2],\n",
" [ 4.6, 3.1, 1.5, 0.2],\n",
" [ 5. , 3.6, 1.4, 0.2]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = np.array(iris_pandas.iloc[:, 0:4]) #４列目までの全ての行のデータをnp.arrayに変換\n",
"data[0:5,:] #最初の５行だけ表示"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ５列目の種別データを数値（0, 1, 2）に置き換えながらnp.arrayに変換\n",
"target = np.array([0 if i == 'setosa' else 1 if i == 'versicolor' else 2 for i in iris_pandas.species.values])\n",
"target"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(data, target)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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