Question7 In machine learning, a confusion matrix is: a procedure to evaluate the speed of the prediction a technique to evaluate the amount of resources used across different machine learning models a technique to measure the accuracy of the machine learning model a table that is often used to describe the performance of a classification model ResetMaximum marks: 0.5 Flag question undefined

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BlackTom AI · Accepted Answer

A confusion matrix is a table that is often used to describe the performance of a classification model.

1) Relevant knowledge points and concepts
- A confusion matrix summarizes prediction results for classification: it shows counts of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) for binary problems; for multiclass problems it generalizes to an N×N table (rows: actual classes, columns: predicted classes or vice versa).
- Common metrics derived from it include accuracy, precision, recall (sensitivity), specificity, and F1 score.

2) Step-by-step reasoning process
- The confusion matrix itself is a table of counts. From these counts you can compute performance metrics that characterize different aspects of model behavior (e.g., whether the model tends to miss positives or incorrectly predict positives).

3) Example calculation with intermediate steps
- Example 2×2 confusion matrix values: TP = 40, FN = 10, FP = 5, TN = 45.
  - Total = TP + FN + FP + TN = 40 + 10 + 5 + 45 = 100.
  - Accuracy = (TP + TN) / Total = (40 + 45) / 100 = 85 / 100 = 0.85 (85%).
  - Precision = TP / (TP + FP) = 40 / (40 + 5) = 40 / 45 ≈ 0.8889 (88.89%).
  - Recall (Sensitivity) = TP / (TP + FN) = 40 / (40 + 10) = 40 / 50 = 0.8 (80%).
  - These calculations show how the table is used to describe multiple performance aspects.

4) Why the correct answer is correct
- The confusion matrix is literally a table used to describe and inspect a classification model’s performance by showing counts of correct and incorrect predictions per class. It is foundational for diagnosing classification errors and for computing detailed metrics beyond a single aggregate accuracy number.

5) Why the other options are incorrect
- “A procedure to evaluate the speed of the prediction”: this refers to latency/benchmarking, not a confusion matrix.
- “A technique to evaluate the amount of resources used”: this concerns computational profiling (CPU/GPU/memory), not confusion matrices.
- “A technique to measure the accuracy of the machine learning model”: accuracy is one metric that can be computed from a confusion matrix, but the matrix itself is not solely an accuracy-measuring technique — it is a table summarizing all prediction outcomes.

6) Conclusion
- Therefore, the correct answer is “a table that is often used to describe the performance of a classification model,” which matches the provided answer.

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