What are the best metrics for evaluating models?

What are the best metrics for evaluating machine learning models?

The best metrics depend on the type of model and the problem. For classification, accuracy, precision, recall, and F1 score are common. For regression, you might use mean absolute error, 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 and is especially useful when you need a single score to evaluate a model's performance.

Are there metrics specific to imbalanced datasets?

Yes, in imbalanced datasets, metrics like the area under the ROC curve (AUC-ROC) and the Matthews correlation coefficient (MCC) can provide better insights into model performance.