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
train, test = train_test_split(df, test_size=0.2)
As such, it is desirable to split the dataset into train and test sets in a way
that preserves the same proportions of examples in each class as observed in the
original dataset.
from sklearn.model_selection import train_test_split
# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3,
random_state=42,stratify=y)
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)from sklearn.model_selection import train_test_split
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.2, random_state = 0)