What are the different validation techniques in machine learning?

What are the different validation techniques in machine learning?

There are several validation techniques, including k-fold cross-validation, stratified sampling, and leave-one-out validation. Each has its pros and cons.

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 tested on the remaining subset. This process is repeated k times.

What are the advantages of using stratified sampling?

Stratified sampling ensures that each class is represented proportionally in both training and testing sets, which can lead to better model performance.

How does leave-one-out validation differ from k-fold?

Leave-one-out validation is a special case of k-fold where k equals the number of data points. Each training set is created by leaving out one data point for testing.