python机器学习《机器学习Python实践》整理,sklearn库应用详解

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Table of Contents

初始

初识机器学习

python机器学习的生态圈

第一个机器学习项目

import numpy as np
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
import pandas as pd

机器学习中的hello world项目

(1)导入数据
(2)概述数据
(3)数据可视化
(4)评估算法
(5)实施预测

#导入类库
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

导入数据

#导入数据
filename=r'iris.data'
names=['separ-length','separ-width','petal-length','petal-width','class']
dataset=pd.read_table(filename,names=names,sep=',')
dataset
separ-lengthsepar-widthpetal-lengthpetal-widthclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
..................
1456.73.05.22.3Iris-virginica
1466.32.55.01.9Iris-virginica
1476.53.05.22.0Iris-virginica
1486.23.45.42.3Iris-virginica
1495.93.05.11.8Iris-virginica

150 rows × 5 columns

概述数据

dataset.skew()
separ-length    0.314911
separ-width     0.334053
petal-length   -0.274464
petal-width    -0.104997
dtype: float64
dataset.hist()
array([[<AxesSubplot:title={'center':'separ-length'}>,
        <AxesSubplot:title={'center':'separ-width'}>],
       [<AxesSubplot:title={'center':'petal-length'}>,
        <AxesSubplot:title={'center':'petal-width'}>]], dtype=object)

dataset.plot(kind='density',subplots=True,layout=(2,2))
array([[<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>],
       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>]],
      dtype=object)

#查看数据维度
dataset.shape
(150, 5)
#查看自身
dataset.head(10)
separ-lengthsepar-widthpetal-lengthpetal-widthclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
55.43.91.70.4Iris-setosa
64.63.41.40.3Iris-setosa
75.03.41.50.2Iris-setosa
84.42.91.40.2Iris-setosa
94.93.11.50.1Iris-setosa
#统计描述数据
dataset.describe()
separ-lengthsepar-widthpetal-lengthpetal-width
count150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.198667
std0.8280660.4335941.7644200.763161
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
#数据分类分布
dataset.groupby('class').count()
separ-lengthsepar-widthpetal-lengthpetal-width
class
Iris-setosa50505050
Iris-versicolor50505050
Iris-virginica50505050

数据可视化

#单变量图表
#箱线图
plt.style.use('seaborn-notebook')
dataset.plot(kind='box',subplots=True,layout=(2,2),sharex=False,sharey=False)
separ-length       AxesSubplot(0.125,0.536818;0.352273x0.343182)
separ-width     AxesSubplot(0.547727,0.536818;0.352273x0.343182)
petal-length          AxesSubplot(0.125,0.125;0.352273x0.343182)
petal-width        AxesSubplot(0.547727,0.125;0.352273x0.343182)
dtype: object

#直方图
dataset.hist()
array([[<AxesSubplot:title={'center':'separ-length'}>,
        <AxesSubplot:title={'center':'separ-width'}>],
       [<AxesSubplot:title={'center':'petal-length'}>,
        <AxesSubplot:title={'center':'petal-width'}>]], dtype=object)

#多变量图表
#散点矩阵图
pd.plotting.scatter_matrix(dataset)
array([[<AxesSubplot:xlabel='separ-length', ylabel='separ-length'>,
        <AxesSubplot:xlabel='separ-width', ylabel='separ-length'>,
        <AxesSubplot:xlabel='petal-length', ylabel='separ-length'>,
        <AxesSubplot:xlabel='petal-width', ylabel='separ-length'>],
       [<AxesSubplot:xlabel='separ-length', ylabel='separ-width'>,
        <AxesSubplot:xlabel='separ-width', ylabel='separ-width'>,
        <AxesSubplot:xlabel='petal-length', ylabel='separ-width'>,
        <AxesSubplot:xlabel='petal-width', ylabel='separ-width'>],
       [<AxesSubplot:xlabel='separ-length', ylabel='petal-length'>,
        <AxesSubplot:xlabel='separ-width', ylabel='petal-length'>,
        <AxesSubplot:xlabel='petal-length', ylabel='petal-length'>,
        <AxesSubplot:xlabel='petal-width', ylabel='petal-length'>],
       [<AxesSubplot:xlabel='separ-length', ylabel='petal-width'>,
        <AxesSubplot:xlabel='separ-width', ylabel='petal-width'>,
        <AxesSubplot:xlabel='petal-length', ylabel='petal-width'>,
        <AxesSubplot:xlabel='petal-width', ylabel='petal-width'>]],
      dtype=object)

评估算法

(1)分离出评估数据集
(2)采用10折交叉验证来评估算法模型
(3)生成6个不同的模型来预测新数据
(4)选择最优模型

分离评估数据集

X=np.array(dataset.iloc[:,0:4])
Y=np.array(dataset.iloc[:,4])
validation_size=0.2
seed=7
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=validation_size,random_state=seed)

创建模型

models={}
models['LR']=LogisticRegression(max_iter=1000)
models['LDA']=LinearDiscriminantAnalysis()
models['KNN']=KNeighborsClassifier()
models['CART']=DecisionTreeClassifier()
models['NB']=GaussianNB()
models['SVM']=SVC()

results=[]
for key in models:
    kfold=KFold(n_splits=10,random_state=seed,shuffle=True)
    cv_results=cross_val_score(models[key],X_train,Y_train,cv=kfold,scoring='accuracy')
    results.append(cv_results)
    print('%s:%f(%f)' %(key,cv_results.mean(),cv_results.std()))
LR:0.983333(0.033333)
LDA:0.975000(0.038188)
KNN:0.983333(0.033333)
CART:0.958333(0.076830)
NB:0.966667(0.040825)
SVM:0.983333(0.033333)

选择最优模型

plt.boxplot(results)
plt.xticks([i+1 for i in range(6)],models.keys())
([<matplotlib.axis.XTick at 0x17d53183250>,
  <matplotlib.axis.XTick at 0x17d53183f40>,
  <matplotlib.axis.XTick at 0x17d531aad00>,
  <matplotlib.axis.XTick at 0x17d53644b80>,
  <matplotlib.axis.XTick at 0x17d5364e0d0>,
  <matplotlib.axis.XTick at 0x17d536448e0>],
 [Text(1, 0, 'LR'),
  Text(2, 0, 'LDA'),
  Text(3, 0, 'KNN'),
  Text(4, 0, 'CART'),
  Text(5, 0, 'NB'),
  Text(6, 0, 'SVM')])

实施预测

svm=SVC()
svm.fit(X=X_train,y=Y_train)
pred=svm.predict(X_test)
accuracy_score(Y_test,pred)
0.8666666666666667
confusion_matrix(Y_test,pred)
array([[ 7,  0,  0],
       [ 0, 10,  2],
       [ 0,  2,  9]], dtype=int64)
print(classification_report(Y_test,pred))
                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00         7
Iris-versicolor       0.83      0.83      0.83        12
 Iris-virginica       0.82      0.82      0.82        11

       accuracy                           0.87        30
      macro avg       0.88      0.88      0.88        30
   weighted avg       0.87      0.87      0.87        30

数据准备

数据预处理

调整数据尺度

from sklearn import datasets
iris=datasets.load_iris()
from sklearn.preprocessing import MinMaxScaler
transformer=MinMaxScaler(feature_range=(0,1))#聚集到0附近,方差为1
newX=transformer.fit_transform(iris.data)
newX
array([[0.22222222, 0.625     , 0.06779661, 0.04166667],
       [0.16666667, 0.41666667, 0.06779661, 0.04166667],
       [0.11111111, 0.5       , 0.05084746, 0.04166667],
       [0.08333333, 0.45833333, 0.08474576, 0.04166667],
       [0.19444444, 0.66666667, 0.06779661, 0.04166667],
       [0.30555556, 0.79166667, 0.11864407, 0.125     ],
       [0.08333333, 0.58333333, 0.06779661, 0.08333333],
       [0.19444444, 0.58333333, 0.08474576, 0.04166667],
       [0.02777778, 0.375     , 0.06779661, 0.04166667],
       [0.16666667, 0.45833333, 0.08474576, 0.        ],
       [0.30555556, 0.70833333, 0.08474576, 0.04166667],
       [0.13888889, 0.58333333, 0.10169492, 0.04166667],
       [0.13888889, 0.41666667, 0.06779661, 0.        ],
       [0.        , 0.41666667, 0.01694915, 0.        ],
       [0.41666667, 0.83333333, 0.03389831, 0.04166667],
       [0.38888889, 1.        , 0.08474576, 0.125     ],
       [0.30555556, 0.79166667, 0.05084746, 0.125     ],
       [0.22222222, 0.625     , 0.06779661, 0.08333333],
       [0.38888889, 0.75      , 0.11864407, 0.08333333],
       [0.22222222, 0.75      , 0.08474576, 0.08333333],
       [0.30555556, 0.58333333, 0.11864407, 0.04166667],
       [0.22222222, 0.70833333, 0.08474576, 0.125     ],
       [0.08333333, 0.66666667, 0.        , 0.04166667],
       [0.22222222, 0.54166667, 0.11864407, 0.16666667],
       [0.13888889, 0.58333333, 0.15254237, 0.04166667],
       [0.19444444, 0.41666667, 0.10169492, 0.04166667],
       [0.19444444, 0.58333333, 0.10169492, 0.125     ],
       [0.25      , 0.625     , 0.08474576, 0.04166667],
       [0.25      , 0.58333333, 0.06779661, 0.04166667],
       [0.11111111, 0.5       , 0.10169492, 0.04166667],
       [0.13888889, 0.45833333, 0.10169492, 0.04166667],
       [0.30555556, 0.58333333, 0.08474576, 0.125     ],
       [0.25      , 0.875     , 0.08474576, 0.        ],
       [0.33333333, 0.91666667, 0.06779661, 0.04166667],
       [0.16666667, 0.45833333, 0.08474576, 0.04166667],
       [0.19444444, 0.5       , 0.03389831, 0.04166667],
       [0.33333333, 0.625     , 0.05084746, 0.04166667],
       [0.16666667, 0.66666667, 0.06779661, 0.        ],
       [0.02777778, 0.41666667, 0.05084746, 0.04166667],
       [0.22222222, 0.58333333, 0.08474576, 0.04166667],
       [0.19444444, 0.625     , 0.05084746, 0.08333333],
       [0.05555556, 0.125     , 0.05084746, 0.08333333],
       [0.02777778, 0.5       , 0.05084746, 0.04166667],
       [0.19444444, 0.625     , 0.10169492, 0.20833333],
       [0.22222222, 0.75      , 0.15254237, 0.125     ],
       [0.13888889, 0.41666667, 0.06779661, 0.08333333],
       [0.22222222, 0.75      , 0.10169492, 0.04166667],
       [0.08333333, 0.5       , 0.06779661, 0.04166667],
       [0.27777778, 0.70833333, 0.08474576, 0.04166667],
       [0.19444444, 0.54166667, 0.06779661, 0.04166667],
       [0.75      , 0.5       , 0.62711864, 0.54166667],
       [0.58333333, 0.5       , 0.59322034, 0.58333333],
       [0.72222222, 0.45833333, 0.66101695, 0.58333333],
       [0.33333333, 0.125     , 0.50847458, 0.5       ],
       [0.61111111, 0.33333333, 0.61016949, 0.58333333],
       [0.38888889, 0.33333333, 0.59322034, 0.5       ],
       [0.55555556, 0.54166667, 0.62711864, 0.625     ],
       [0.16666667, 0.16666667, 0.38983051, 0.375     ],
       [0.63888889, 0.375     , 0.61016949, 0.5       ],
       [0.25      , 0.29166667, 0.49152542, 0.54166667],
       [0.19444444, 0.        , 0.42372881, 0.375     ],
       [0.44444444, 0.41666667, 0.54237288, 0.58333333],
       [0.47222222, 0.08333333, 0.50847458, 0.375     ],
       [0.5       , 0.375     , 0.62711864, 0.54166667],
       [0.36111111, 0.375     , 0.44067797, 0.5       ],
       [0.66666667, 0.45833333, 0.57627119, 0.54166667],
       [0.36111111, 0.41666667, 0.59322034, 0.58333333],
       [0.41666667, 0.29166667, 0.52542373, 0.375     ],
       [0.52777778, 0.08333333, 0.59322034, 0.58333333],
       [0.36111111, 0.20833333, 0.49152542, 0.41666667],
       [0.44444444, 0.5       , 0.6440678 , 0.70833333],
       [0.5       , 0.33333333, 0.50847458, 0.5       ],
       [0.55555556, 0.20833333, 0.66101695, 0.58333333],
       [0.5       , 0.33333333, 0.62711864, 0.45833333],
       [0.58333333, 0.375     , 0.55932203, 0.5       ],
       [0.63888889, 0.41666667, 0.57627119, 0.54166667],
       [0.69444444, 0.33333333, 0.6440678 , 0.54166667],
       [0.66666667, 0.41666667, 0.6779661 , 0.66666667],
       [0.47222222, 0.375     , 0.59322034, 0.58333333],
       [0.38888889, 0.25      , 0.42372881, 0.375     ],
       [0.33333333, 0.16666667, 0.47457627, 0.41666667],
       [0.33333333, 0.16666667, 0.45762712, 0.375     ],
       [0.41666667, 0.29166667, 0.49152542, 0.45833333],
       [0.47222222, 0.29166667, 0.69491525, 0.625     ],
       [0.30555556, 0.41666667, 0.59322034, 0.58333333],
       [0.47222222, 0.58333333, 0.59322034, 0.625     ],
       [0.66666667, 0.45833333, 0.62711864, 0.58333333],
       [0.55555556, 0.125     , 0.57627119, 0.5       ],
       [0.36111111, 0.41666667, 0.52542373, 0.5       ],
       [0.33333333, 0.20833333, 0.50847458, 0.5       ],
       [0.33333333, 0.25      , 0.57627119, 0.45833333],
       [0.5       , 0.41666667, 0.61016949, 0.54166667],
       [0.41666667, 0.25      , 0.50847458, 0.45833333],
       [0.19444444, 0.125     , 0.38983051, 0.375     ],
       [0.36111111, 0.29166667, 0.54237288, 0.5       ],
       [0.38888889, 0.41666667, 0.54237288, 0.45833333],
       [0.38888889, 0.375     , 0.54237288, 0.5       ],
       [0.52777778, 0.375     , 0.55932203, 0.5       ],
       [0.22222222, 0.20833333, 0.33898305, 0.41666667],
       [0.38888889, 0.33333333, 0.52542373, 0.5       ],
       [0.55555556, 0.54166667, 0.84745763, 1.        ],
       [0.41666667, 0.29166667, 0.69491525, 0.75      ],
       [0.77777778, 0.41666667, 0.83050847, 0.83333333],
       [0.55555556, 0.375     , 0.77966102, 0.70833333],
       [0.61111111, 0.41666667, 0.81355932, 0.875     ],
       [0.91666667, 0.41666667, 0.94915254, 0.83333333],
       [0.16666667, 0.20833333, 0.59322034, 0.66666667],
       [0.83333333, 0.375     , 0.89830508, 0.70833333],
       [0.66666667, 0.20833333, 0.81355932, 0.70833333],
       [0.80555556, 0.66666667, 0.86440678, 1.        ],
       [0.61111111, 0.5       , 0.69491525, 0.79166667],
       [0.58333333, 0.29166667, 0.72881356, 0.75      ],
       [0.69444444, 0.41666667, 0.76271186, 0.83333333],
       [0.38888889, 0.20833333, 0.6779661 , 0.79166667],
       [0.41666667, 0.33333333, 0.69491525, 0.95833333],
       [0.58333333, 0.5       , 0.72881356, 0.91666667],
       [0.61111111, 0.41666667, 0.76271186, 0.70833333],
       [0.94444444, 0.75      , 0.96610169, 0.875     ],
       [0.94444444, 0.25      , 1.        , 0.91666667],
       [0.47222222, 0.08333333, 0.6779661 , 0.58333333],
       [0.72222222, 0.5       , 0.79661017, 0.91666667],
       [0.36111111, 0.33333333, 0.66101695, 0.79166667],
       [0.94444444, 0.33333333, 0.96610169, 0.79166667],
       [0.55555556, 0.29166667, 0.66101695, 0.70833333],
       [0.66666667, 0.54166667, 0.79661017, 0.83333333],
       [0.80555556, 0.5       , 0.84745763, 0.70833333],
       [0.52777778, 0.33333333, 0.6440678 , 0.70833333],
       [0.5       , 0.41666667, 0.66101695, 0.70833333],
       [0.58333333, 0.33333333, 0.77966102, 0.83333333],
       [0.80555556, 0.41666667, 0.81355932, 0.625     ],
       [0.86111111, 0.33333333, 0.86440678, 0.75      ],
       [1.        , 0.75      , 0.91525424, 0.79166667],
       [0.58333333, 0.33333333, 0.77966102, 0.875     ],
       [0.55555556, 0.33333333, 0.69491525, 0.58333333],
       [0.5       , 0.25      , 0.77966102, 0.54166667],
       [0.94444444, 0.41666667, 0.86440678, 0.91666667],
       [0.55555556, 0.58333333, 0.77966102, 0.95833333],
       [0.58333333, 0.45833333, 0.76271186, 0.70833333],
       [0.47222222, 0.41666667, 0.6440678 , 0.70833333],
       [0.72222222, 0.45833333, 0.74576271, 0.83333333],
       [0.66666667, 0.45833333, 0.77966102, 0.95833333],
       [0.72222222, 0.45833333, 0.69491525, 0.91666667],
       [0.41666667, 0.29166667, 0.69491525, 0.75      ],
       [0.69444444, 0.5       , 0.83050847, 0.91666667],
       [0.66666667, 0.54166667, 0.79661017, 1.        ],
       [0.66666667, 0.41666667, 0.71186441, 0.91666667],
       [0.55555556, 0.20833333, 0.6779661 , 0.75      ],
       [0.61111111, 0.41666667, 0.71186441, 0.79166667],
       [0.52777778, 0.58333333, 0.74576271, 0.91666667],
       [0.44444444, 0.41666667, 0.69491525, 0.70833333]])

正态化数据

from sklearn.preprocessing import StandardScaler
transformer=StandardScaler()
newX=transformer.fit_transform(iris.data)
newX
array([[-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,
        -1.31544430e+00],
       [-1.14301691e+00, -1.31979479e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-1.38535265e+00,  3.28414053e-01, -1.39706395e+00,
        -1.31544430e+00],
       [-1.50652052e+00,  9.82172869e-02, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  1.24920112e+00, -1.34022653e+00,
        -1.31544430e+00],
       [-5.37177559e-01,  1.93979142e+00, -1.16971425e+00,
        -1.05217993e+00],
       [-1.50652052e+00,  7.88807586e-01, -1.34022653e+00,
        -1.18381211e+00],
       [-1.02184904e+00,  7.88807586e-01, -1.28338910e+00,
        -1.31544430e+00],
       [-1.74885626e+00, -3.62176246e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,
        -1.44707648e+00],
       [-5.37177559e-01,  1.47939788e+00, -1.28338910e+00,
        -1.31544430e+00],
       [-1.26418478e+00,  7.88807586e-01, -1.22655167e+00,
        -1.31544430e+00],
       [-1.26418478e+00, -1.31979479e-01, -1.34022653e+00,
        -1.44707648e+00],
       [-1.87002413e+00, -1.31979479e-01, -1.51073881e+00,
        -1.44707648e+00],
       [-5.25060772e-02,  2.16998818e+00, -1.45390138e+00,
        -1.31544430e+00],
       [-1.73673948e-01,  3.09077525e+00, -1.28338910e+00,
        -1.05217993e+00],
       [-5.37177559e-01,  1.93979142e+00, -1.39706395e+00,
        -1.05217993e+00],
       [-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,
        -1.18381211e+00],
       [-1.73673948e-01,  1.70959465e+00, -1.16971425e+00,
        -1.18381211e+00],
       [-9.00681170e-01,  1.70959465e+00, -1.28338910e+00,
        -1.18381211e+00],
       [-5.37177559e-01,  7.88807586e-01, -1.16971425e+00,
        -1.31544430e+00],
       [-9.00681170e-01,  1.47939788e+00, -1.28338910e+00,
        -1.05217993e+00],
       [-1.50652052e+00,  1.24920112e+00, -1.56757623e+00,
        -1.31544430e+00],
       [-9.00681170e-01,  5.58610819e-01, -1.16971425e+00,
        -9.20547742e-01],
       [-1.26418478e+00,  7.88807586e-01, -1.05603939e+00,
        -1.31544430e+00],
       [-1.02184904e+00, -1.31979479e-01, -1.22655167e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  7.88807586e-01, -1.22655167e+00,
        -1.05217993e+00],
       [-7.79513300e-01,  1.01900435e+00, -1.28338910e+00,
        -1.31544430e+00],
       [-7.79513300e-01,  7.88807586e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-1.38535265e+00,  3.28414053e-01, -1.22655167e+00,
        -1.31544430e+00],
       [-1.26418478e+00,  9.82172869e-02, -1.22655167e+00,
        -1.31544430e+00],
       [-5.37177559e-01,  7.88807586e-01, -1.28338910e+00,
        -1.05217993e+00],
       [-7.79513300e-01,  2.40018495e+00, -1.28338910e+00,
        -1.44707648e+00],
       [-4.16009689e-01,  2.63038172e+00, -1.34022653e+00,
        -1.31544430e+00],
       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  3.28414053e-01, -1.45390138e+00,
        -1.31544430e+00],
       [-4.16009689e-01,  1.01900435e+00, -1.39706395e+00,
        -1.31544430e+00],
       [-1.14301691e+00,  1.24920112e+00, -1.34022653e+00,
        -1.44707648e+00],
       [-1.74885626e+00, -1.31979479e-01, -1.39706395e+00,
        -1.31544430e+00],
       [-9.00681170e-01,  7.88807586e-01, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  1.01900435e+00, -1.39706395e+00,
        -1.18381211e+00],
       [-1.62768839e+00, -1.74335684e+00, -1.39706395e+00,
        -1.18381211e+00],
       [-1.74885626e+00,  3.28414053e-01, -1.39706395e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  1.01900435e+00, -1.22655167e+00,
        -7.88915558e-01],
       [-9.00681170e-01,  1.70959465e+00, -1.05603939e+00,
        -1.05217993e+00],
       [-1.26418478e+00, -1.31979479e-01, -1.34022653e+00,
        -1.18381211e+00],
       [-9.00681170e-01,  1.70959465e+00, -1.22655167e+00,
        -1.31544430e+00],
       [-1.50652052e+00,  3.28414053e-01, -1.34022653e+00,
        -1.31544430e+00],
       [-6.58345429e-01,  1.47939788e+00, -1.28338910e+00,
        -1.31544430e+00],
       [-1.02184904e+00,  5.58610819e-01, -1.34022653e+00,
        -1.31544430e+00],
       [ 1.40150837e+00,  3.28414053e-01,  5.35408562e-01,
         2.64141916e-01],
       [ 6.74501145e-01,  3.28414053e-01,  4.21733708e-01,
         3.95774101e-01],
       [ 1.28034050e+00,  9.82172869e-02,  6.49083415e-01,
         3.95774101e-01],
       [-4.16009689e-01, -1.74335684e+00,  1.37546573e-01,
         1.32509732e-01],
       [ 7.95669016e-01, -5.92373012e-01,  4.78571135e-01,
         3.95774101e-01],
       [-1.73673948e-01, -5.92373012e-01,  4.21733708e-01,
         1.32509732e-01],
       [ 5.53333275e-01,  5.58610819e-01,  5.35408562e-01,
         5.27406285e-01],
       [-1.14301691e+00, -1.51316008e+00, -2.60315415e-01,
        -2.62386821e-01],
       [ 9.16836886e-01, -3.62176246e-01,  4.78571135e-01,
         1.32509732e-01],
       [-7.79513300e-01, -8.22569778e-01,  8.07091462e-02,
         2.64141916e-01],
       [-1.02184904e+00, -2.43394714e+00, -1.46640561e-01,
        -2.62386821e-01],
       [ 6.86617933e-02, -1.31979479e-01,  2.51221427e-01,
         3.95774101e-01],
       [ 1.89829664e-01, -1.97355361e+00,  1.37546573e-01,
        -2.62386821e-01],
       [ 3.10997534e-01, -3.62176246e-01,  5.35408562e-01,
         2.64141916e-01],
       [-2.94841818e-01, -3.62176246e-01, -8.98031345e-02,
         1.32509732e-01],
       [ 1.03800476e+00,  9.82172869e-02,  3.64896281e-01,
         2.64141916e-01],
       [-2.94841818e-01, -1.31979479e-01,  4.21733708e-01,
         3.95774101e-01],
       [-5.25060772e-02, -8.22569778e-01,  1.94384000e-01,
        -2.62386821e-01],
       [ 4.32165405e-01, -1.97355361e+00,  4.21733708e-01,
         3.95774101e-01],
       [-2.94841818e-01, -1.28296331e+00,  8.07091462e-02,
        -1.30754636e-01],
       [ 6.86617933e-02,  3.28414053e-01,  5.92245988e-01,
         7.90670654e-01],
       [ 3.10997534e-01, -5.92373012e-01,  1.37546573e-01,
         1.32509732e-01],
       [ 5.53333275e-01, -1.28296331e+00,  6.49083415e-01,
         3.95774101e-01],
       [ 3.10997534e-01, -5.92373012e-01,  5.35408562e-01,
         8.77547895e-04],
       [ 6.74501145e-01, -3.62176246e-01,  3.08058854e-01,
         1.32509732e-01],
       [ 9.16836886e-01, -1.31979479e-01,  3.64896281e-01,
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       [ 1.15917263e+00, -5.92373012e-01,  5.92245988e-01,
         2.64141916e-01],
       [ 1.03800476e+00, -1.31979479e-01,  7.05920842e-01,
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         3.95774101e-01],
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        -2.62386821e-01],
       [-4.16009689e-01, -1.51316008e+00,  2.38717193e-02,
        -1.30754636e-01],
       [-4.16009689e-01, -1.51316008e+00, -3.29657076e-02,
        -2.62386821e-01],
       [-5.25060772e-02, -8.22569778e-01,  8.07091462e-02,
         8.77547895e-04],
       [ 1.89829664e-01, -8.22569778e-01,  7.62758269e-01,
         5.27406285e-01],
       [-5.37177559e-01, -1.31979479e-01,  4.21733708e-01,
         3.95774101e-01],
       [ 1.89829664e-01,  7.88807586e-01,  4.21733708e-01,
         5.27406285e-01],
       [ 1.03800476e+00,  9.82172869e-02,  5.35408562e-01,
         3.95774101e-01],
       [ 5.53333275e-01, -1.74335684e+00,  3.64896281e-01,
         1.32509732e-01],
       [-2.94841818e-01, -1.31979479e-01,  1.94384000e-01,
         1.32509732e-01],
       [-4.16009689e-01, -1.28296331e+00,  1.37546573e-01,
         1.32509732e-01],
       [-4.16009689e-01, -1.05276654e+00,  3.64896281e-01,
         8.77547895e-04],
       [ 3.10997534e-01, -1.31979479e-01,  4.78571135e-01,
         2.64141916e-01],
       [-5.25060772e-02, -1.05276654e+00,  1.37546573e-01,
         8.77547895e-04],
       [-1.02184904e+00, -1.74335684e+00, -2.60315415e-01,
        -2.62386821e-01],
       [-2.94841818e-01, -8.22569778e-01,  2.51221427e-01,
         1.32509732e-01],
       [-1.73673948e-01, -1.31979479e-01,  2.51221427e-01,
         8.77547895e-04],
       [-1.73673948e-01, -3.62176246e-01,  2.51221427e-01,
         1.32509732e-01],
       [ 4.32165405e-01, -3.62176246e-01,  3.08058854e-01,
         1.32509732e-01],
       [-9.00681170e-01, -1.28296331e+00, -4.30827696e-01,
        -1.30754636e-01],
       [-1.73673948e-01, -5.92373012e-01,  1.94384000e-01,
         1.32509732e-01],
       [ 5.53333275e-01,  5.58610819e-01,  1.27429511e+00,
         1.71209594e+00],
       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,
         9.22302838e-01],
       [ 1.52267624e+00, -1.31979479e-01,  1.21745768e+00,
         1.18556721e+00],
       [ 5.53333275e-01, -3.62176246e-01,  1.04694540e+00,
         7.90670654e-01],
       [ 7.95669016e-01, -1.31979479e-01,  1.16062026e+00,
         1.31719939e+00],
       [ 2.12851559e+00, -1.31979479e-01,  1.61531967e+00,
         1.18556721e+00],
       [-1.14301691e+00, -1.28296331e+00,  4.21733708e-01,
         6.59038469e-01],
       [ 1.76501198e+00, -3.62176246e-01,  1.44480739e+00,
         7.90670654e-01],
       [ 1.03800476e+00, -1.28296331e+00,  1.16062026e+00,
         7.90670654e-01],
       [ 1.64384411e+00,  1.24920112e+00,  1.33113254e+00,
         1.71209594e+00],
       [ 7.95669016e-01,  3.28414053e-01,  7.62758269e-01,
         1.05393502e+00],
       [ 6.74501145e-01, -8.22569778e-01,  8.76433123e-01,
         9.22302838e-01],
       [ 1.15917263e+00, -1.31979479e-01,  9.90107977e-01,
         1.18556721e+00],
       [-1.73673948e-01, -1.28296331e+00,  7.05920842e-01,
         1.05393502e+00],
       [-5.25060772e-02, -5.92373012e-01,  7.62758269e-01,
         1.58046376e+00],
       [ 6.74501145e-01,  3.28414053e-01,  8.76433123e-01,
         1.44883158e+00],
       [ 7.95669016e-01, -1.31979479e-01,  9.90107977e-01,
         7.90670654e-01],
       [ 2.24968346e+00,  1.70959465e+00,  1.67215710e+00,
         1.31719939e+00],
       [ 2.24968346e+00, -1.05276654e+00,  1.78583195e+00,
         1.44883158e+00],
       [ 1.89829664e-01, -1.97355361e+00,  7.05920842e-01,
         3.95774101e-01],
       [ 1.28034050e+00,  3.28414053e-01,  1.10378283e+00,
         1.44883158e+00],
       [-2.94841818e-01, -5.92373012e-01,  6.49083415e-01,
         1.05393502e+00],
       [ 2.24968346e+00, -5.92373012e-01,  1.67215710e+00,
         1.05393502e+00],
       [ 5.53333275e-01, -8.22569778e-01,  6.49083415e-01,
         7.90670654e-01],
       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,
         1.18556721e+00],
       [ 1.64384411e+00,  3.28414053e-01,  1.27429511e+00,
         7.90670654e-01],
       [ 4.32165405e-01, -5.92373012e-01,  5.92245988e-01,
         7.90670654e-01],
       [ 3.10997534e-01, -1.31979479e-01,  6.49083415e-01,
         7.90670654e-01],
       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,
         1.18556721e+00],
       [ 1.64384411e+00, -1.31979479e-01,  1.16062026e+00,
         5.27406285e-01],
       [ 1.88617985e+00, -5.92373012e-01,  1.33113254e+00,
         9.22302838e-01],
       [ 2.49201920e+00,  1.70959465e+00,  1.50164482e+00,
         1.05393502e+00],
       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,
         1.31719939e+00],
       [ 5.53333275e-01, -5.92373012e-01,  7.62758269e-01,
         3.95774101e-01],
       [ 3.10997534e-01, -1.05276654e+00,  1.04694540e+00,
         2.64141916e-01],
       [ 2.24968346e+00, -1.31979479e-01,  1.33113254e+00,
         1.44883158e+00],
       [ 5.53333275e-01,  7.88807586e-01,  1.04694540e+00,
         1.58046376e+00],
       [ 6.74501145e-01,  9.82172869e-02,  9.90107977e-01,
         7.90670654e-01],
       [ 1.89829664e-01, -1.31979479e-01,  5.92245988e-01,
         7.90670654e-01],
       [ 1.28034050e+00,  9.82172869e-02,  9.33270550e-01,
         1.18556721e+00],
       [ 1.03800476e+00,  9.82172869e-02,  1.04694540e+00,
         1.58046376e+00],
       [ 1.28034050e+00,  9.82172869e-02,  7.62758269e-01,
         1.44883158e+00],
       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,
         9.22302838e-01],
       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,
         1.44883158e+00],
       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,
         1.71209594e+00],
       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,
         1.44883158e+00],
       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,
         9.22302838e-01],
       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,
         1.05393502e+00],
       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,
         1.44883158e+00],
       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,
         7.90670654e-01]])

标准化数据

from sklearn.preprocessing import Normalizer
transformer=Normalizer()
newX=transformer.fit_transform(iris.data)
newX
array([[0.80377277, 0.55160877, 0.22064351, 0.0315205 ],
       [0.82813287, 0.50702013, 0.23660939, 0.03380134],
       [0.80533308, 0.54831188, 0.2227517 , 0.03426949],
       [0.80003025, 0.53915082, 0.26087943, 0.03478392],
       [0.790965  , 0.5694948 , 0.2214702 , 0.0316386 ],
       [0.78417499, 0.5663486 , 0.2468699 , 0.05808704],
       [0.78010936, 0.57660257, 0.23742459, 0.0508767 ],
       [0.80218492, 0.54548574, 0.24065548, 0.0320874 ],
       [0.80642366, 0.5315065 , 0.25658935, 0.03665562],
       [0.81803119, 0.51752994, 0.25041771, 0.01669451],
       [0.80373519, 0.55070744, 0.22325977, 0.02976797],
       [0.786991  , 0.55745196, 0.26233033, 0.03279129],
       [0.82307218, 0.51442011, 0.24006272, 0.01714734],
       [0.8025126 , 0.55989251, 0.20529392, 0.01866308],
       [0.81120865, 0.55945424, 0.16783627, 0.02797271],
       [0.77381111, 0.59732787, 0.2036345 , 0.05430253],
       [0.79428944, 0.57365349, 0.19121783, 0.05883625],
       [0.80327412, 0.55126656, 0.22050662, 0.04725142],
       [0.8068282 , 0.53788547, 0.24063297, 0.04246464],
       [0.77964883, 0.58091482, 0.22930848, 0.0458617 ],
       [0.8173379 , 0.51462016, 0.25731008, 0.03027177],
       [0.78591858, 0.57017622, 0.23115252, 0.06164067],
       [0.77577075, 0.60712493, 0.16864581, 0.03372916],
       [0.80597792, 0.52151512, 0.26865931, 0.07901744],
       [0.776114  , 0.54974742, 0.30721179, 0.03233808],
       [0.82647451, 0.4958847 , 0.26447184, 0.03305898],
       [0.79778206, 0.5424918 , 0.25529026, 0.06382256],
       [0.80641965, 0.54278246, 0.23262105, 0.03101614],
       [0.81609427, 0.5336001 , 0.21971769, 0.03138824],
       [0.79524064, 0.54144043, 0.27072022, 0.03384003],
       [0.80846584, 0.52213419, 0.26948861, 0.03368608],
       [0.82225028, 0.51771314, 0.22840286, 0.06090743],
       [0.76578311, 0.60379053, 0.22089897, 0.0147266 ],
       [0.77867447, 0.59462414, 0.19820805, 0.02831544],
       [0.81768942, 0.51731371, 0.25031309, 0.03337508],
       [0.82512295, 0.52807869, 0.19802951, 0.03300492],
       [0.82699754, 0.52627116, 0.19547215, 0.03007264],
       [0.78523221, 0.5769053 , 0.22435206, 0.01602515],
       [0.80212413, 0.54690282, 0.23699122, 0.03646019],
       [0.80779568, 0.53853046, 0.23758697, 0.03167826],
       [0.80033301, 0.56023311, 0.20808658, 0.04801998],
       [0.86093857, 0.44003527, 0.24871559, 0.0573959 ],
       [0.78609038, 0.57170209, 0.23225397, 0.03573138],
       [0.78889479, 0.55222635, 0.25244633, 0.09466737],
       [0.76693897, 0.57144472, 0.28572236, 0.06015208],
       [0.82210585, 0.51381615, 0.23978087, 0.05138162],
       [0.77729093, 0.57915795, 0.24385598, 0.030482  ],
       [0.79594782, 0.55370283, 0.24224499, 0.03460643],
       [0.79837025, 0.55735281, 0.22595384, 0.03012718],
       [0.81228363, 0.5361072 , 0.22743942, 0.03249135],
       [0.76701103, 0.35063361, 0.51499312, 0.15340221],
       [0.74549757, 0.37274878, 0.52417798, 0.17472599],
       [0.75519285, 0.33928954, 0.53629637, 0.16417236],
       [0.75384916, 0.31524601, 0.54825394, 0.17818253],
       [0.7581754 , 0.32659863, 0.5365549 , 0.17496355],
       [0.72232962, 0.35482858, 0.57026022, 0.16474184],
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       [0.73260391, 0.36029701, 0.55245541, 0.1681386 ],
       [0.76262994, 0.3

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