Instruction: Define a confusion matrix and explain its components.
Context: This question tests the candidate's familiarity with a fundamental tool in evaluating classification model performance.
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The way I'd explain it in an interview is this: A confusion matrix is a table that compares predicted classes with actual classes so you can see exactly where the classifier is making mistakes. In binary classification, that usually means true positives, false positives, true negatives, and false...