What are some common validation errors in AI models?

What are some common validation errors in AI models?

Common validation errors include overfitting, underfitting, and data leakage. Would you like examples of each?

Yes, please provide examples of overfitting and underfitting.

Overfitting occurs when a model learns noise in the training data, performing poorly on new data. Underfitting happens when a model is too simple to capture the underlying trend.

How can I prevent these validation errors?

To prevent overfitting, use techniques like cross-validation and regularization. For underfitting, consider using more complex models or feature engineering.

Can you give me a case study of a validation error?

Sure! One case study involved a healthcare model that overfitted to historical data, leading to poor predictions on new patient data.