How do I implement K-Fold cross-validation in Python?

How do I implement K-Fold cross-validation in Python?

You can use the KFold class from the sklearn.model_selection module. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

from sklearn.model_selection import KFold
import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])
Y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

kf = KFold(n_splits=5)
for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    Y_train, Y_test = Y[train_index], Y[test_index]
    # Train your model here

What if I want to use Stratified K-Fold instead?

You can use StratifiedKFold from the same module. It ensures that each fold has the same proportion of classes as the whole dataset. Here's how:

from sklearn.model_selection import StratifiedKFold

skf = StratifiedKFold(n_splits=5)
for train_index, test_index in skf.split(X, Y):
    X_train, X_test = X[train_index], X[test_index]
    Y_train, Y_test = Y[train_index], Y[test_index]
    # Train your model here