Cross-Validation in ML:

Boost Accuracy & Avoid Overfitting!

What is Cross-Validation?

Cross-validation estimates how well a ML model generalises to new, unseen data. Essential!

Avoid overfitting! CV gives realistic performance. Tune hyperparameters accurately.

Why Use It?

Data split into K "folds." K-1 train, 1 test. Rotate! Average the results.

Popular choice. Good balance: computation vs. reliability. Repeat several times.

Common Type: 5-Fold CV

Ensures each fold has similar class distribution. Ideal for imbalanced datasets.

Stratified K-Fold

Key! Properly split data before feature scaling. Prevent biased evaluation!

CV Avoids Data Leakage

Use Cross-Validation Now!

Essential for building robust, generalisable machine learning models. Start today!