What are some common mistakes in AI model validation?

What are some common mistakes in AI model validation?

Some common mistakes include not using a validation set, overfitting to the training data, and ignoring data leakage.

Can you explain what data leakage is?

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

How can I prevent overfitting in my model?

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

What is cross-validation?

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