What is stratified K-fold cross-validation and how do I implement it?

What is stratified K-fold cross-validation and how do I implement it?

Stratified K-fold cross-validation is a technique used to ensure that each fold of your dataset has the same proportion of classes as the entire dataset. This is particularly useful for imbalanced datasets. To implement it, you can use libraries like scikit-learn in Python.

Can you show me a code example for implementing it in Python?

Sure! Here's a simple example using scikit-learn:

from sklearn.model_selection import StratifiedKFold
import numpy as np

X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])
y = np.array([0, 0, 0, 1, 1, 1, 0, 0, 1, 1])

skf = StratifiedKFold(n_splits=5)
for train_index, test_index in skf.split(X, y):
    print("TRAIN:", train_index, "TEST:", test_index)

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

The main advantage of stratified K-fold is that it maintains the class distribution in each fold, which helps in providing a more reliable estimate of model performance, especially in cases of class imbalance.

Are there any libraries that can help with this implementation?

Yes, the most commonly used library for this purpose is scikit-learn in Python. It provides a built-in function for stratified K-fold cross-validation, making it easy to implement.