What are some common errors in model validation?

What are some common errors in model validation?

Common errors in model validation include overfitting, underfitting, data leakage, and improper cross-validation techniques.

Can you explain overfitting and how to avoid it?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. To avoid it, use techniques like regularization, pruning, or cross-validation.

What is data leakage and why is it a problem?

Data leakage happens when information from outside the training dataset is used to create the model, leading to overly optimistic performance metrics. It undermines the model's ability to generalize.

How can I implement proper cross-validation?

To implement proper cross-validation, you can use techniques like k-fold cross-validation, where the dataset is divided into k subsets, and the model is trained and validated k times, each time using a different subset for validation.