What does a confusion matrix provide in model evaluation?

What does a confusion matrix provide in model evaluation?

Step 1: Understand that a confusion matrix is a table used to evaluate the performance of a classification model.
Step 2: Know that it shows four important values: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).
Step 3: True positives (TP) are the cases where the model correctly predicts the positive class.
Step 4: True negatives (TN) are the cases where the model correctly predicts the negative class.
Step 5: False positives (FP) are the cases where the model incorrectly predicts the positive class when it is actually negative.
Step 6: False negatives (FN) are the cases where the model incorrectly predicts the negative class when it is actually positive.
Step 7: By analyzing these four values, you can understand how well your model is performing and where it is making mistakes.

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