How do validation techniques affect model performance?

How do validation techniques affect model performance?

Validation techniques are crucial as they help assess how well a model generalizes to unseen data, impacting its performance.

What are some common validation techniques used in machine learning?

Common techniques include k-fold cross-validation, holdout validation, and stratified sampling, each serving different purposes.

Can you explain k-fold cross-validation in detail?

Sure! In k-fold cross-validation, the dataset is divided into k subsets. The model is trained on k-1 subsets and validated on the remaining one, repeating this process k times.

How does the choice of validation technique influence overfitting?

Choosing the right validation technique helps identify overfitting by ensuring the model performs well on unseen data, not just the training set.