What are some advanced validation techniques in machine learning?

What are some advanced validation techniques in machine learning?

Advanced validation techniques include k-fold cross-validation, stratified sampling, and bootstrapping. Would you like to know more about any of these?

Can you explain k-fold cross-validation?

Sure! K-fold cross-validation involves splitting the dataset into 'k' subsets. The model is trained on 'k-1' subsets and validated on the remaining subset. This process is repeated 'k' times, ensuring each subset is used for validation once.

What is the benefit of using stratified sampling?

Stratified sampling ensures that each class is proportionally represented in both the training and validation sets, which helps improve the model's performance on imbalanced datasets.

How does bootstrapping work in validation?

Bootstrapping involves repeatedly sampling from the dataset with replacement to create multiple training sets. This allows for estimating the model's performance and variability.