What is the purpose of the confusion matrix?

What is the purpose of the confusion matrix?

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, false positives, true negatives, and false negatives.
Step 3: True positives are the cases where the model correctly predicts the positive class.
Step 4: False positives are the cases where the model incorrectly predicts the positive class when it is actually negative.
Step 5: True negatives are the cases where the model correctly predicts the negative class.
Step 6: False negatives are the cases where the model incorrectly predicts the negative class when it is actually positive.
Step 7: By analyzing these values, you can understand how well your model is performing.

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