What are the effects of different validation techniques in machine learning?

What are the effects of different validation techniques in machine learning?

Different validation techniques help assess model performance, prevent overfitting, and ensure generalization.

Can you explain the difference between k-fold and stratified k-fold validation?

K-fold validation splits the dataset into k subsets, while stratified k-fold ensures each fold has the same proportion of classes.

How does cross-validation improve model accuracy?

Cross-validation provides a more reliable estimate of model performance by using multiple training and testing splits.

What are some common pitfalls when using validation techniques?

Common pitfalls include data leakage, not using enough folds, and failing to consider class imbalance.

Which validation technique is best for small datasets?

Leave-one-out cross-validation (LOOCV) is often best for small datasets as it maximizes training data usage.