How do I evaluate machine learning algorithms?

What are the best methods to evaluate machine learning algorithms?

The best methods include cross-validation, confusion matrix, ROC curves, and precision-recall metrics.

Can you explain cross-validation in more detail?

Sure! Cross-validation involves partitioning the data into subsets, training the model on some subsets, and validating it on others.

What is a confusion matrix?

A confusion matrix is a table used to evaluate the performance of a classification model, showing true vs. predicted classifications.

How do I choose the right evaluation metric?

Choosing the right metric depends on your specific problem. For example, use accuracy for balanced classes and F1 score for imbalanced classes.