What are the different validation techniques in machine learning?

What are the different validation techniques in machine learning?

There are several validation techniques in machine learning, including k-fold cross-validation, stratified sampling, and leave-one-out validation. Would you like to know more about any specific technique?

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, with each subset used as the validation set once.

What are the advantages of using stratified sampling?

Stratified sampling ensures that each class is proportionally represented in both the training and validation sets, which can lead to better model performance, especially in imbalanced datasets.

How does leave-one-out validation work?

Leave-one-out validation is a special case of k-fold cross-validation where 'k' equals the number of data points. Each time, one data point is used for validation while the rest are used for training. This can be computationally expensive but provides a thorough evaluation.