What are some effective data splitting strategies for machine learning?

What are some effective data splitting strategies for machine learning?

Common strategies include train-test split, k-fold cross-validation, and stratified sampling.

Can you explain k-fold cross-validation in more detail?

Sure! K-fold cross-validation involves dividing the dataset into k subsets. The model is trained on k-1 subsets and tested on the remaining one, repeating this process k times.

What are the advantages of using stratified sampling?

Stratified sampling ensures that each class is proportionally represented in both training and testing sets, improving model performance.

How do I choose the right split ratio for my dataset?

The split ratio often depends on the dataset size and the problem type, but a common ratio is 80% training and 20% testing.