What are the best practices for creating test sets in machine learning?

What are the best practices for creating test sets in machine learning?

Creating effective test sets involves several best practices, such as ensuring diversity in data, avoiding data leakage, and maintaining a representative sample of the overall dataset.

Can you explain what data leakage is?

Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance estimates.

How can I ensure my test set is representative?

To ensure your test set is representative, you should stratify your data based on key features and ensure it reflects the distribution of the overall dataset.

What size should my test set be?

A common practice is to allocate 20-30% of your dataset for testing, but the size can vary based on the total dataset size and complexity.

Any tools you recommend for creating test sets?

Yes, tools like Scikit-learn, TensorFlow, and Pandas can help you split your data effectively and manage your test sets.