Why is validation important in machine learning?

Why is validation important in machine learning?

Validation is crucial in machine learning as it helps ensure that the model generalizes well to unseen data.

What are the different types of validation techniques?

Common validation techniques include k-fold cross-validation, holdout validation, and stratified sampling.

How does overfitting relate to validation?

Overfitting occurs when a model learns noise in the training data. Validation helps detect this by testing on unseen data.

Can you explain k-fold cross-validation?

Sure! K-fold cross-validation splits the dataset into k subsets, training the model k times, each time using a different subset for validation.

What happens if I skip validation?

Skipping validation can lead to models that perform well on training data but fail to generalize, resulting in poor performance on new data.