How do I calculate accuracy from a confusion matrix?

How do I calculate accuracy from a confusion matrix?

To calculate accuracy, you can use the formula: Accuracy = (TP + TN) / (TP + TN + FP + FN). Here, TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives.

Can you explain what TP, TN, FP, and FN mean?

Sure! TP (True Positives) are correctly predicted positive cases, TN (True Negatives) are correctly predicted negative cases, FP (False Positives) are incorrectly predicted positive cases, and FN (False Negatives) are incorrectly predicted negative cases.

What if I have a multi-class confusion matrix?

In a multi-class confusion matrix, you calculate accuracy the same way, but you sum the true positives and true negatives across all classes.

Is accuracy always a good metric to use?

Not always! Accuracy can be misleading, especially in imbalanced datasets. It's often better to look at precision, recall, or F1-score.