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).

Question

BlackTom AI · Accepted Answer

Classifiers are commonly tuned to prefer minimizing either false positives or false negatives depending on domain-specific costs, so the statement is True.

1) Relevant knowledge points and concepts
- Confusion matrix terms: true positive (TP), false positive (FP), true negative (TN), false negative (FN).
- Precision = TP / (TP + FP); Recall (sensitivity) = TP / (TP + FN).
- There is a trade-off between precision and recall as the decision threshold or loss weighting changes.
- Practical techniques: adjusting decision threshold, class weighting, cost-sensitive loss, resampling, or using different evaluation metrics (ROC/PR, Fβ) to select models.

2) Step-by-step reasoning
- Different applications assign different costs to FP vs FN. For life-critical detection (cancer screening) a FN (missed disease) is much more costly than an occasional FP; for spam filtering, a FP (legitimate mail marked spam) is more costly than letting a spam message through.
- To bias toward fewer FNs (higher recall), lower the decision threshold or increase positive-class weight so the model predicts positive more often. To bias toward fewer FPs (higher precision), raise the threshold or penalize false positives more heavily.
- These adjustments change TP, FP, FN, TN counts and thus precision/recall.

3) Example calculation (illustrating the trade-off)
- Suppose 100 samples, 10 positives, 90 negatives.
- High threshold: predicted positives = 5 → TP=4, FP=1, FN=6, TN=89. Precision = 4/5 = 0.80; Recall = 4/10 = 0.40.
- Lower threshold: predicted positives = 12 → TP=9, FP=3, FN=1, TN=87. Precision = 9/12 = 0.75; Recall = 9/10 = 0.90.
- Lowering the threshold increased recall (fewer FNs) at the cost of slightly lower precision (more FPs), showing the deliberate bias.

4) Why the correct answer is correct
- Because the relative cost of FP vs FN drives model tuning decisions, practitioners explicitly bias models (thresholds, loss functions) to favor the metric that matters most for the use case.

5) Why the other option is incorrect
- Answer "False" would imply that model tuning never favors one type of error over another; that ignores real-world cost asymmetries and standard practices (thresholding, class weighting, cost-sensitive learning), so it is incorrect.

6) Conclusion
- The correct answer is True, which matches the provided answer.

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).True/False

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。

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

Which ONE of the following thalamic nuclei is targeted by the trigeminothalamic tract?

Which ONE of the following is disinhibited in the direct pathway of the basal ganglia motor loop

30. What is the main purpose of the lecture?

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