What is the primary purpose of a confusion matrix in classification?

Options

  • A. To summarize classification results using measures such as true positives, true negatives, false positives, and false negatives
  • B. To reduce the number of features in a dataset
  • C. To calculate only the training time of a model
  • D. To divide continuous data into clusters
  • E. None of the above

Correct Answer (Detailed Explanation is Below)

A. To summarize classification results using measures such as true positives, true negatives, false positives, and false negatives

Detailed Explanation

A confusion matrix is used to evaluate a classification model by comparing predicted classes with actual classes. It commonly contains True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) values. These values are used to calculate metrics such as accuracy, precision, recall, and F1-score.

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