What are the best metrics for evaluating machine learning 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 RMSE, MAE, 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 is F1 score and when should I use it?

The F1 score is the harmonic mean of precision and recall. It's useful when you need a balance between precision and recall, especially in imbalanced datasets.

Are there any metrics for evaluating clustering models?

Yes! For clustering, you can use metrics like silhouette score, Davies-Bouldin index, and adjusted Rand index to evaluate the quality of clusters.