What are confusion matrix metrics and how do I interpret them?

What are confusion matrix metrics and how do I interpret them?

Confusion matrix metrics are used to evaluate the performance of a classification model. They include accuracy, precision, recall, and F1 score.

Can you explain what accuracy means in this context?

Accuracy is the ratio of correctly predicted instances to the total instances. It shows how often the model is correct.

What about precision and recall? How are they different?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances. They provide different insights into model performance.

How do I calculate the F1 score?

The F1 score is the harmonic mean of precision and recall. It can be calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall).