What does a confusion matrix provide?

What does a confusion matrix provide?

Step 1: Understand that a confusion matrix is a table used to evaluate the performance of a classification model.
Step 2: Identify the four key terms in the confusion matrix: true positives, false positives, true negatives, and false negatives.
Step 3: True positives (TP) are the cases where the model correctly predicts the positive class.
Step 4: False positives (FP) are the cases where the model incorrectly predicts the positive class when it is actually negative.
Step 5: True negatives (TN) are the cases where the model correctly predicts the negative class.
Step 6: False negatives (FN) are the cases where the model incorrectly predicts the negative class when it is actually positive.
Step 7: The confusion matrix summarizes these four outcomes to help assess how well the model is performing.

No concepts available.