Which metric answers the question: “Of the items the model said were positive, how many actually were positive?”

Question

BlackTom AI · Accepted Answer

Precision measures the proportion of predicted positives that are truly positive, so it directly answers “Of the items the model said were positive, how many actually were positive?”

1) Relevant knowledge points and concepts
- Confusion matrix entries: True Positives (TP), False Positives (FP), True Negatives (TN), False Negatives (FN).
- Precision (also called Positive Predictive Value): precision = TP / (TP + FP).
- Recall (sensitivity): recall = TP / (TP + FN).
- False Positive Rate (FPR): FPR = FP / (FP + TN).
- Accuracy: accuracy = (TP + TN) / (TP + TN + FP + FN).

2) Step‑by‑step reasoning
- The phrase “Of the items the model said were positive” refers to all predicted positives = TP + FP.
- The phrase “how many actually were positive?” asks for the fraction of those that are true positives = TP.
- Therefore compute TP divided by (TP + FP), which is exactly the definition of precision.

3) Example calculation with intermediate steps
- Suppose TP = 80 and FP = 20.
- Predicted positives = TP + FP = 80 + 20 = 100.
- Correctly predicted positives = TP = 80.
- Precision = TP / (TP + FP) = 80 / 100 = 0.80 → 80%.

4) Why the correct answer is correct
- Precision directly measures the correctness among predicted positives, matching the question wording exactly. It answers the conditional probability P(actual positive | predicted positive).

5) Why the other options are incorrect (brief)
- Recall: measures P(predicted positive | actual positive) = TP / (TP + FN); it answers “Of actual positives, how many did the model find?” not the asked conditional.
- False Positive Rate: measures FP among actual negatives = FP / (FP + TN); unrelated to “of predicted positives.”
- Accuracy: measures overall correct predictions among all instances = (TP + TN) / total; it mixes positives and negatives and does not isolate predicted positives.

6) Conclusion
This reasoning shows the correct metric is Precision, matching the provided answer: Precision.

Which metric answers the question: “Of the items the model said were positive, how many actually were positive?” Single choice

Similar Questions

以下哪些是评价推荐系统准确率的方法？（多选题） Which of the following are ways to evaluate the accuracy of a recommender system? Select all that apply

Question 31

医疗人工智能模型用于检测500名患者的疾病：实际上有100名患者患有这种疾病模型正确识别了其中70种它错误地将50名健康患者诊断为患病问题：计算模型的精度。计算型号的召回率。

Sometimes we have to tune a model to bias more towards a certain type of error or performance metric (e.g., false positives, false negatives, precision, recall) because of the nature of a specific use case (e.g., cancer detection/prediction vs spam detection).

Is the following statement true or false? In the products of methylation-hydrolysis, every -OH group corresponds to the position of a glycosidic bond in the starting polysaccharide.

Which of the follwoing structures represents amylopectin?

Which of the following is true about an aldopentose?I. It is a monosaccharide.II. It contains a CHO groupIII. It is a disaccharide.IV. It is an oligosaccharide.

For stock A, we have 𝛽 𝑖 = 0.70. Suppose the expected market risk premium next year is 9% and the risk-free rate is 3%. What is the expected return of this stock based on the CAPM? (Please answer in % and round to 2 decimal places. If the answer is 8.057%, then in the box, write 8.06)

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler