In the context of evaluation metrics, what is a confusion matrix?

In the context of evaluation metrics, what is a confusion matrix?

Step 1: Understand that a confusion matrix is a table used to evaluate how well a classification algorithm is performing.
Step 2: Know that the matrix has four key components: 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 helps visualize these outcomes and assess the accuracy of the model.

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