sklearn split train test

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showing results for - "sklearn split train test"
Violette
01 Jan 2017
1from sklearn.model_selection import train_test_split
2
3X = df.drop(['target'],axis=1).values   # independant features
4y = df['target'].values					# dependant variable
5
6# Choose your test size to split between training and testing sets:
7X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
source
Juan José
06 Apr 2017
1import numpy as np
2from sklearn.model_selection import train_test_split
3
4X, y = np.arange(10).reshape((5, 2)), range(5)
5
6X_train, X_test, y_train, y_test = train_test_split(
7    X, y, test_size=0.33, random_state=42)
8
9X_train
10# array([[4, 5],
11#        [0, 1],
12#        [6, 7]])
13
14y_train
15# [2, 0, 3]
16
17X_test
18# array([[2, 3],
19#        [8, 9]])
20
21y_test
22# [1, 4]
source
Soline
01 Feb 2019
1 import numpy as np
2 from sklearn.model_selection import train_test_split
3
4
5X_train, X_test, y_train, y_test = train_test_split(
6  X, y, test_size=0.33, random_state=42
7)
source
Lizzie
02 Feb 2020
1train, validate, test = np.split(df.sample(frac=1), [int(.6*len(df)), int(.8*len(df))])
2
Gaia
05 Jan 2017
1from sklearn.model_selection import train_test_split
2X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
3print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
Till
21 Feb 2017
1from sklearn.model_selection import train_test_split
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