What are some common data splitting methods in machine learning?

What are some common data splitting methods in machine learning?

Common data splitting methods 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 divides the 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 involves splitting the dataset into 'k' subsets. The model is trained on 'k-1' subsets and tested on the remaining one, repeating this process 'k' times.

Why is stratified sampling important?

Stratified sampling ensures that each class is proportionally represented in both the training and testing sets, which helps improve model performance.