What is a confusion matrix and how do I interpret it?

What is a confusion matrix and how do I interpret it?

A confusion matrix is a table used to evaluate the performance of a classification model. It shows the true positives, false positives, true negatives, and false negatives.

Can you explain what true positives and false negatives mean?

Sure! True positives are the cases where the model correctly predicts the positive class, while false negatives are the cases where the model incorrectly predicts the negative class when it should have predicted positive.

How can 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 some limitations of using a confusion matrix?

Some limitations include that it doesn't provide insights into the types of errors made and can be misleading if the classes are imbalanced.