Train dev test split sklearn

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)

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
)

Train dev test split sklearn

train, validate, test = np.split(df.sample(frac=1), [int(.6*len(df)), int(.8*len(df))])

Source: datascience.stackexchange.com

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