What are the risks associated with validation in AI models?

What are the risks associated with validation in AI models?

The main risks include overfitting, underfitting, and reliance on biased data, which can lead to inaccurate predictions.

Can you explain overfitting and how it affects model performance?

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

What steps can I take to mitigate these risks during validation?

You can use techniques like cross-validation, regularization, and ensuring a diverse training dataset to mitigate these risks.

Are there specific metrics I should focus on during validation?

Yes, metrics like accuracy, precision, recall, and F1 score are important, but also consider the context of your application.

How can I ensure my validation data is unbiased?

To ensure unbiased validation data, use stratified sampling and ensure representation from all relevant groups in your dataset.