Train test split sklearn

from sklearn.model_selection import train_test_split

X = df.drop(['target'],axis=1).values   # independant features
y = df['target'].values					# dependant variable

# Choose your test size to split between training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

Code for test and train split

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
 X, y, test_size=0.33, random_state=42)

Source: scikit-learn.org

Train test split python

from sklearn.model_selection import train_test_split
				
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

Sklearn train_test_split

import numpy as np from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
 X, y, test_size=0.33, random_state=42)

How to distribute a dataset in train and test using scikit

from sklearn.model_selection import train_test_split
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.2, random_state = 0)

Train test split sklearn

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

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