What are some effective data-splitting techniques for machine learning?

What are some effective data-splitting techniques for machine learning?

Some effective data-splitting techniques include train-test split, k-fold cross-validation, and stratified sampling.

Can you explain the train-test split method?

Sure! The train-test split method involves dividing your dataset into two parts: one for training the model and the other for testing its performance.

What is k-fold cross-validation?

K-fold cross-validation is a technique where the dataset is divided into 'k' subsets. The model is trained on 'k-1' subsets and tested on the remaining one, repeating this process 'k' times.

When should I use stratified sampling?

Stratified sampling is useful when you want to ensure that each class is proportionally represented in both the training and testing sets, especially in imbalanced datasets.