What are the best practices in model validation?

What are the best practices in model validation?

Best practices in model validation include using a separate validation dataset, cross-validation, and ensuring reproducibility.

Can you explain why cross-validation is important?

Cross-validation helps to assess how the results of a statistical analysis will generalize to an independent dataset.

What techniques can I use for model validation?

Common techniques include k-fold cross-validation, stratified sampling, and holdout validation.

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

Choosing the right technique depends on your dataset size, model complexity, and the specific goals of your analysis.