What is a confusion matrix in AI performance metrics?

What is a confusion matrix in AI performance metrics?

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.

How do I interpret the values in a confusion matrix?

In a confusion matrix, true positives (TP) are correctly predicted positive cases, false positives (FP) are incorrectly predicted as positive, true negatives (TN) are correctly predicted negative cases, and false negatives (FN) are incorrectly predicted as negative.

What metrics can I derive from a confusion matrix?

You can derive several metrics, including accuracy, precision, recall, and F1 score, which help assess the model's performance.

Can you give an example of a confusion matrix?

Sure! For a binary classification, a confusion matrix might look like this:

| | Predicted Positive | Predicted Negative | |----------------|--------------------|--------------------| | Actual Positive| 50 (TP) | 10 (FN) | | Actual Negative| 5 (FP) | 100 (TN) |