sklearn import train_test_split code example

Example 1: 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)

Example 2: sklearn split train test

import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.arange(10).reshape((5, 2)), range(5)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42)

X_train
# array([[4, 5],
#        [0, 1],
#        [6, 7]])

y_train
# [2, 0, 3]

X_test
# array([[2, 3],
#        [8, 9]])

y_test
# [1, 4]

Example 3: sklearn train test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

Example 4: train_size

You have to specify this parameter only if you’re not specifying the test_size. This is the same as test_size, but instead you tell the class what percent of the dataset you want to split as the training set.

Example 5: 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)

Example 6: scikit learn train test 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)