What is a confusion matrix and how does it relate to accuracy?

What is a confusion matrix and how does it relate to accuracy?

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

Can you explain how to calculate accuracy from a confusion matrix?

Sure! Accuracy is calculated as the sum of true positives and true negatives divided by the total number of predictions. The formula is: Accuracy = (TP + TN) / (TP + TN + FP + FN).

What do TP, TN, FP, and FN stand for?

TP stands for True Positives, TN for True Negatives, FP for False Positives, and FN for False Negatives. They represent the outcomes of predictions.

Why is accuracy not always the best metric to use?

Accuracy can be misleading, especially in imbalanced datasets. It may not reflect the model's performance on minority classes, so other metrics like precision, recall, or F1-score are often more informative.