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

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 of these sets?

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 class is properly represented.

What tools can I use for data splitting?

You can use libraries like Scikit-learn in Python, which provides functions for easy data splitting.