What is model validation in machine learning?

What is model validation in machine learning?

Model validation is the process of evaluating a machine learning model's performance on unseen data to ensure it generalizes well.

Why is model validation important?

It's crucial because it helps prevent overfitting, ensuring that the model performs well on new, unseen data.

What are some common techniques for model validation?

Common techniques include cross-validation, train-test split, and using validation datasets.

How do I choose the right validation technique for my model?

It depends on your dataset size and the model complexity. Cross-validation is often preferred for smaller datasets.