How does validation affect AI performance?

How does validation affect AI performance?

Validation is crucial as it helps ensure that the AI model generalizes well to unseen data, improving its accuracy.

What are some common validation techniques used in AI?

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

Can poor validation lead to overfitting?

Yes, inadequate validation can result in overfitting, where the model performs well on training data but poorly on new data.

How often should I validate my AI models?

It's recommended to validate your models regularly, especially after significant changes to the data or model architecture.

What metrics should I use to evaluate validation results?

Metrics like accuracy, precision, recall, and F1 score are commonly used to evaluate validation results.