[sklearn] data set split sklearn moduel_ selection. train_ test_ split

function

X_train,X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, 
                                                                           test_size=None, 
                                                                           train_size=None, 
                                                                           random_state=None, 
                                                                           shuffle=True, 
                                                                           stratify=None)

Parameters ：

• X： Sample feature set to be divided
• y： Label of the sample to be divided
• test_size： If in 0~1 Between , Is the ratio of the number of samples in the test set to the number of original samples ; If integer , Is the number of test set samples .
• random_state： Random number seed , Default not set . If it is not set , The test data set segmented from each run is random ; Set it up , Each run is divided into the same result . Different seeds have different results , The results of the same seed running are the same .

Return value

• X_train Divided training set data （ Return value ）
• X_test Divided test set data （ Return value ）
• y_train Divided training set label （ Return value ）
• y_test Divided test set label （ Return value ）

give an example ：

import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.arange(20).reshape((10, 2)), np.arange(10)
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=12)

print('X\n',X)
print('\n y \n',y)
print('\n X_train \n',X_train)
print('\n X_test \n',X_test)
print('\n y_train \n',y_train)
print('\n y_test \n',y_test)