What is the significance of the confusion matrix in model evaluation?

What is the significance of the confusion matrix in model evaluation?

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 (TP), false positives (FP), true negatives (TN), and false negatives (FN).
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: Use the confusion matrix to calculate important metrics like accuracy, precision, recall, and F1 score to assess the model's performance.

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