What are common issues in model validation?

What are common issues in model validation?

Common issues include overfitting, underfitting, data leakage, and not using a representative validation set.

How can I prevent overfitting during validation?

You can prevent overfitting by using techniques like cross-validation, regularization, and simplifying your model.

What is data leakage and how does it affect validation?

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

Can you explain the importance of a representative validation set?

A representative validation set ensures that the model's performance is evaluated on data that reflects real-world scenarios, improving generalization.