What is a confusion matrix in machine learning?

What is a confusion matrix in machine learning?

A confusion matrix is a table used to evaluate the performance of a classification model. It summarizes the correct and incorrect predictions.

Can you explain the different components of a confusion matrix?

Sure! A confusion matrix typically includes True Positives, True Negatives, False Positives, and False Negatives.

How do I calculate accuracy from a confusion matrix?

Accuracy can be calculated as (True Positives + True Negatives) / Total Predictions. It gives you the proportion of correct predictions.

What are precision and recall, and how are they related to the confusion matrix?

Precision is the ratio of True Positives to the total predicted positives, while recall is the ratio of True Positives to actual positives. Both are derived from the confusion matrix.