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mosquito三颗猫 · 2022年01月05日

这个问题能翻译下吗?

* 问题详情,请 查看题干

NO.PZ202108310100000105

问题如下:

Based on Exhibit 1, which confusion matrix demonstrates the most favorable value of the performance metric that best addresses Azarov’s concern?

选项:

A.

Confusion Matrix A

B.

Confusion Matrix B

C.

Confusion Matrix C

解释:

A is correct.

Precision is the ratio of correctly predicted positive classes to all predicted positive classes and is useful in situations where the cost of false positives or Type I errors is high.

Confusion Matrix A has the highest precision and therefore demonstrates the most favorable value of the performance metric that best addresses Azarov’s concern about the cost of Type I errors.

Confusion Matrix A has a precision score of 0.95, which is higher than the precision scores of Confusion Matrix B (0.93) and Confusion Matrix C (0.86).

B is incorrect because precision, not accuracy, is the performance measure that best addresses Azarov’s concern about the cost of Type I errors.

Confusion Matrix B demonstrates the most favorable value for the accuracy score (0.92), which is higher than the accuracy scores of Confusion Matrix A (0.91) and Confusion Matrix C (0.91).

Accuracy is a performance measure that gives equal weight to false positives and false negatives and is considered an appropriate performance measure when the class distribution in the dataset is equal (a balanced dataset).

However, Azarov is most concerned with the cost of false positives, or Type I errors, and not with finding the equilibrium between precision and recall.

Furthermore, Dataset XYZ has an unequal (unbalanced) class distribution between positive sentiment and negative sentiment sentences.

C is incorrect because precision, not recall or F1 score, is the performance measure that best addresses Azarov’s concern about the cost of Type I errors.

Confusion Matrix C demonstrates the most favorable value for the recall score (0.97), which is higher than the recall scores of Confusion Matrix A (0.87) and Confusion Matrix B (0.90).

Recall is the ratio of correctly predicted positive classes to all actual positive classes and is useful in situations where the cost of false negatives, or Type II errors, is high.

However, Azarov is most concerned with the cost of Type I errors, not Type II errors.

F1 score is more appropriate (than accuracy) when there is unequal class distribution in the dataset and it is necessary to measure the equilibrium of precision and recall.

Confusion Matrix C demonstrates the most favorable value for the F1 score (0.92), which is higher than the F1 scores of Confusion Matrix A (0.91) and Confusion Matrix B (0.91).

Although Dataset XYZ has an unequal class distribution between positive sentiment and negative sentiment sentences, Azarov is most concerned with the cost of false positives, or Type I errors, and not with finding the equilibrium between precision and recall.

这个问题能翻译下吗?

1 个答案

星星_品职助教 · 2022年01月06日

同学你好,

题干中Azarov’s concern是  “the cost of Type I errors.” 衡量“Tyoe I error”最合适的指标是Precision

所以直接选择Precision最高的confusion matrix A就可以了。

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同样,提问需要具体,以后提问需要写明到底是哪里没有看懂和需要解释。

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NO.PZ202108310100000105问题如下 Baseon Exhibit 1, whiconfusion matrix monstrates the most favorable value of the performanmetric thbest aresses Azarov’s concern? A.Confusion Matrix B.Confusion Matrix C.Confusion Matrix A is correct. Precision is the ratio of correctly prectepositive classes to all prectepositive classes anis useful in situations where the cost of false positives or Type I errors is high. Confusion Matrix A hthe highest precision antherefore monstrates the most favorable value of the performanmetric thbest aresses Azarov’s concern about the cost of Type I errors.Confusion Matrix A ha precision score of 0.95, whiis higher ththe precision scores of Confusion Matrix B (0.93) anConfusion Matrix C (0.86).B is incorrebecause precision, not accuracy, is the performanmeasure thbest aresses Azarov’s concern about the cost of Type I errors. Confusion Matrix B monstrates the most favorable value for the accurascore (0.92), whiis higher ththe accurascores of Confusion Matrix A (0.91) anConfusion Matrix C (0.91). Accurais a performanmeasure thgives equweight to false positives anfalse negatives anis consireappropriate performanmeasure when the class stribution in the taset is equ(a balancetaset). However, Azarov is most concernewith the cost of false positives, or Type I errors, annot with finng the equilibrium between precision anrecall. Furthermore, taset XYZ hunequ(unbalance class stribution between positive sentiment annegative sentiment sentences.C is incorrebecause precision, not recall or F1 score, is the performanmeasure thbest aresses Azarov’s concern about the cost of Type I errors. Confusion Matrix C monstrates the most favorable value for the recall score (0.97), whiis higher ththe recall scores of Confusion Matrix A (0.87) anConfusion Matrix B (0.90). Recall is the ratio of correctly prectepositive classes to all actupositive classes anis useful in situations where the cost of false negatives, or Type II errors, is high. However, Azarov is most concernewith the cost of Type I errors, not Type II errors.F1 score is more appropriate (thaccuracy) when there is unequclass stribution in the taset anit is necessary to measure the equilibrium of precision anrecall. Confusion Matrix C monstrates the most favorable value for the F1 score (0.92), whiis higher ththe F1 scores of Confusion Matrix A (0.91) anConfusion Matrix B (0.91). Although taset XYZ hunequclass stribution between positive sentiment annegative sentiment sentences, Azarov is most concernewith the cost of false positives, or Type I errors, annot with finng the equilibrium between precision anrecall. 老师,之前我们讲过,type I error 是拒真错误,实际是positive的预测为negative,这样理解的话就应该是FN代表一类错误,反之FP代表type II error(即实际是negative的预测为positive),但这样理解就和讲义中346页里的不一致了,我不太理解,能帮忙详细解答以下吗

2022-04-09 17:19 1 · 回答