What are the key metrics for classification and regression models?

What are the key metrics for classification and regression models?

For classification models, key metrics include accuracy, precision, recall, and F1 score. For regression, we often use mean squared error (MSE), mean absolute error (MAE), and R-squared.

Can you explain what precision and recall mean?

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.

How do I choose the right metric for my model?

Choosing the right metric depends on your specific problem. For example, if false positives are costly, prioritize precision. If missing a positive case is critical, focus on recall.

What about R-squared in regression?

R-squared indicates how well the independent variables explain the variability of the dependent variable. A higher R-squared value means a better fit.