What are some common issues in model evaluation?

What are some common issues in model evaluation?

Common issues include overfitting, underfitting, data leakage, and not using appropriate metrics.

Can you explain overfitting and why it's a problem?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. This leads to poor performance on unseen data.

What can I do to prevent overfitting?

You can use techniques like cross-validation, regularization, and pruning to help prevent overfitting.

What about data leakage? How does it affect evaluation?

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