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)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)from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2)
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)import numpy as np
from sklearn.model_selection import train_test_split
# Data example
X, y = np.arange(10).reshape((5, 2)), range(5)
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
from sklearn.model_selection import train_test_split
# Split into training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)