What are the best practices for train-test split in machine learning?

What are the best practices for train-test split in machine learning?

The best practices for train-test split include using a 70-80% training set and 20-30% test set, ensuring randomness, and stratifying if necessary.

Why is it important to stratify the data?

Stratifying ensures that each class is proportionally represented in both the training and test sets, which helps in better model evaluation.

How do I implement train-test split in Python?

You can use the train_test_split function from sklearn.model_selection. It allows you to easily split your dataset.

What should I do if my dataset is very small?

For small datasets, consider using techniques like cross-validation or bootstrapping to make the most of your data.