What does the term 'confusion matrix' refer to in classification tasks?

What does the term 'confusion matrix' refer to in classification tasks?

Step 1: Understand that a confusion matrix is a tool used to evaluate how well a classification model is performing.
Step 2: Know that it is presented in a table format.
Step 3: Identify the four key terms in the confusion matrix: true positives, false positives, true negatives, and false negatives.
Step 4: True positives (TP) are the cases where the model correctly predicts the positive class.
Step 5: False positives (FP) are the cases where the model incorrectly predicts the positive class when it is actually negative.
Step 6: True negatives (TN) are the cases where the model correctly predicts the negative class.
Step 7: False negatives (FN) are the cases where the model incorrectly predicts the negative class when it is actually positive.
Step 8: The confusion matrix helps visualize these outcomes and assess the model's accuracy.

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