What is stratified K-fold cross-validation?

Can you explain what stratified K-fold cross-validation is?

Stratified K-fold cross-validation is a technique used to evaluate machine learning models by ensuring that each fold has a representative distribution of the target variable.

Why is stratified K-fold important?

It's important because it helps to prevent bias in the model evaluation, especially in cases where the target classes are imbalanced.

Can you give me an example of how to implement it in Python?

Sure! You can use the StratifiedKFold class from the sklearn.model_selection module to implement it. Here's a simple example:

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]

What are the advantages of using stratified K-fold over regular K-fold?

The main advantage is that it maintains the proportion of classes in each fold, leading to a more reliable estimate of model performance.