What are the best evaluation metrics for different AI models?

What are the best evaluation metrics for different AI models?

The best evaluation metrics depend on the type of model and the task. For classification, accuracy, precision, recall, and F1 score are common. For regression, you might use mean squared error or R-squared.

Can you explain precision and recall?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances. High precision means fewer false positives, and high recall means fewer false negatives.

What about F1 score? How is it calculated?

The F1 score is the harmonic mean of precision and recall. It balances the two metrics, especially when you have an uneven class distribution. It's calculated as 2 * (precision * recall) / (precision + recall).

Are there specific metrics for deep learning models?

Yes, for deep learning models, you might also consider metrics like AUC-ROC for binary classification, and for multi-class problems, you can use categorical accuracy or top-k accuracy.