How do I evaluate the performance of my machine learning algorithm?

How do I evaluate the performance of my machine learning algorithm?

You can evaluate your algorithm's performance using metrics like accuracy, precision, recall, and F1 score.

What is the difference between precision and recall?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances.

When should I use F1 score instead of accuracy?

Use F1 score when you have an imbalanced dataset, as it considers both precision and recall.

Can you explain how to implement cross-validation?

Cross-validation involves splitting your dataset into training and testing sets multiple times to ensure robust performance evaluation.