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xxuannx · 2021年12月24日

可以解释下这道题吗 不太明白为什么不能选其他指标

NO.PZ2021083101000006

问题如下:

Bector turns his attention to Dataset XYZ, containing 84,000 tokens and 10,000 sentences. Bector chooses an appropriate feature selection method to identify and remove unnecessary tokens from the dataset and then focuses on model training.

For performance evaluation purposes, Dataset XYZ is split into a training set, crossvalidation (CV) set, and test set. Each of the sentences has already been labeled as either a positive sentiment (Class “1”) or a negative sentiment (Class “0”) sentence.

There is an unequal class distribution between the positive sentiment and negative sentiment sentences in Dataset XYZ. Simple random sampling is applied within levels of the sentiment class labels to balance the class distributions within the splits.

Bector’s view is that the false positive and false negative evaluation metrics should be given equal weight.

Based only on Dataset XYZ’s composition and Bector’s view regarding false positive and false negative evaluation metrics, which performance measure is most appropriate?

选项:

A.

Recall

B.

F1 score

C.

Precision

解释:

B is correct.

F1 score is the most appropriate performance measure for Dataset XYZ. Bector gives equal weight to false positives and false negatives. Accuracy and F1 score are overall performance measures that give equal weight to false positives and false negatives.

Accuracy is considered an appropriate performance measure for balanced datasets, where the number of “1” and “0” classes are equal.

F1 score is considered 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.

Since Dataset XYZ contains an unequal class distribution between positive and negative sentiment sentences, F1 score is the most appropriate performance measure.

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.

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.

考点:Model Training - Performance Evaluation

可以解释下这道题吗 不太明白为什么不能选其他指标

3 个答案
已采纳答案

星星_品职助教 · 2021年12月24日

同学你好,

本题要选“most appropriate”的方法,由于题干中提示了“There is an unequal class distribution between the positive sentiment and negative sentiment sentences in Dataset XYZ”,所以对应出这是F1 score的特性。

星星_品职助教 · 2022年12月20日

@immaculate

可以理解为不同的类别相差的比较大

immaculate · 2022年12月20日

unequal class discribution中文 不相等的类别分类意思么

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NO.PZ2021083101000006 问题如下 Bector turns his attention to taset XYZ, containing 84,000 tokens an10,000 sentences. Bector chooses appropriate feature selection methoto intify anremove unnecessary tokens from the taset anthen focuses on mol training. For performanevaluation purposes, taset XYZ is split into a training set, crossvalition (CV) set, antest set. Eaof the sentences halrea been labeleeither a positive sentiment (Class “1”) or a negative sentiment (Class “0”) sentence. There is unequclass stribution between the positive sentiment annegative sentiment sentences in taset XYZ. Simple ranm sampling is appliewithin levels of the sentiment class labels to balanthe class stributions within the splits. Bector’s view is ththe false positive anfalse negative evaluation metrishoulgiven equweight. Baseonly on taset XYZ’s composition anBector’s view regarng false positive anfalse negative evaluation metrics, whiperformanmeasure is most appropriate? A.Recall B.F1 score C.Precision B is correct. F1 score is the most appropriate performanmeasure for taset XYZ. Bector gives equweight to false positives anfalse negatives. AccuraanF1 score are overall performanmeasures thgive equweight to false positives anfalse negatives. Accurais consireappropriate performanmeasure for balancetasets, where the number of “1” an“0” classes are equal. F1 score is consiremore appropriate thaccurawhen there is unequclass stribution in the taset anit is necessary to measure the equilibrium of precision anrecall. Sintaset XYZ contains unequclass stribution between positive annegative sentiment sentences, F1 score is the most appropriate performanmeasure.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. 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. 考点Mol Training - PerformanEvaluation 基于uneven class前提,F1确实是最适合的,但是基于问题中的FN部分F1是如何考虑?因为题目问regarng false positive anfalse negative evaluation metrics,以下哪个方法适用。麻烦解惑,谢谢

2024-01-12 12:41 1 · 回答