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A data scientist is using the following confusion matrix to assess model performance: Actually Fails Actually Succeeds Predicted to Fail 80% 20% Predicted to Succeed 15% 85% The model is predicting whether a delivery truck will be able to make 200 scheduled delivery stops. Every time the model is correct, the company saves 1 hour in planning and scheduling. Every time the model is wrong, the company loses 4 hours of delivery time. Which of the following is the net model impact for the company?
Correct Answer: D
First, we assume 100 trucks (or 100 predictions), as the percentages are easiest to scale on a base of 100. Using the confusion matrix: * True Positives (Predicted Fail & Actually Fails): 80 trucks - correct # +1 hr each = +80 hrs * False Positives (Predicted Fail & Actually Succeeds): 20 trucks - incorrect # -4 hrs each = -80 hrs * False Negatives (Predicted Succeed & Actually Fails): 15 trucks - incorrect # -4 hrs each = -60 hrs * True Negatives (Predicted Succeed & Actually Succeeds): 85 trucks - correct # +1 hr each = +85 hrs Now calculate net hours: Total gain: 80 hrs (TP) + 85 hrs (TN) = +165 hrs Total loss: 80 hrs (FP) + 60 hrs (FN) = -140 hrs Net Impact: 165 - 140 = +25 hours saved So the correct answer is: B : (25 hours saved) However, based on the table provided (which appears to be normalized as percentages), the values apply to a total of 100 predictions. Let's recalculate carefully and validate. Breakdown: * TP = 80% # 80 × +1 hr = +80 hrs * FP = 20% # 20 × -4 hrs = -80 hrs * FN = 15% # 15 × -4 hrs = -60 hrs * TN = 85% # 85 × +1 hr = +85 hrs Total hours = +80 + 85 - 80 - 60 = +25 hrs Final answer: B. 25 hours saved Official References: * CompTIA DataX (DY0-001) Study Guide - Section 4.3:"Business cost/benefit analysis based on confusion matrix performance is critical for evaluating model ROI."
