What are the best practices for data splitting?

What are the best practices for data splitting in machine learning?

The best practices for data splitting include using a training set, validation set, and test set to ensure model generalization.

How do I determine the right size for each data split?

A common approach is to use 70% for training, 15% for validation, and 15% for testing, but it can vary based on your dataset.

Should I stratify my data when splitting?

Yes, stratifying your data is important, especially for imbalanced datasets, to ensure each split represents the overall distribution.

What tools can help with data splitting?

Tools like Scikit-learn in Python provide functions like train_test_split to easily manage data splitting.

Are there any common mistakes to avoid when splitting data?

Common mistakes include not shuffling the data, using the same data for training and testing, and not considering data leakage.