How do I choose the right metrics for evaluating my machine learning model?

How do I choose the right metrics for evaluating my machine learning model?

Choosing the right metrics depends on your model's purpose. Are you focusing on classification, regression, or something else?

I'm working on a classification problem. What metrics should I consider?

For classification, consider accuracy, precision, recall, F1 score, and AUC-ROC. Each has its strengths depending on your goals.

What if I have an imbalanced dataset?

In that case, precision, recall, and F1 score are more informative than accuracy, as they better reflect model performance on minority classes.

Can you explain AUC-ROC in simple terms?

Sure! AUC-ROC measures the model's ability to distinguish between classes. A higher AUC indicates better performance.