Which indicators are used for model evaluation in classification? (Select all that apply)

Question

BlackTom AI · Accepted Answer

Question focus: identifying evaluation metrics used for classification models. We evaluate each option to see whether it serves as a performance indicator in classification tasks.
Option: PCA
- PCA stands for Principal Component Analysis and is primarily a dimensionality reduction technique used for data preprocessing, visualization, or to reduce feature space. It is not an evaluation metric, so it does not measure how well a classifier performs. Therefore, this option is not appropriate as an evaluation indicator for classification.
Option: Precision
- Precision measures the proportion of correctly predicted positive instances among all instances predicted as positive. It directly reflects a classifier’s accuracy on its positive predictions and is a standard evaluation metric in classification, especially when the cost of false positives is high. This is a valid evaluation indicator for classification.
Option: Recall
- Recall (also called sensitivity or true positive rate) assesses the proportion of actual positives that were correctly identified by the model. It complements precision by focusing on the model’s ability to capture positive cases, and is a standard metric in classification problems, particularly when missing positive cases is costly. This is a valid evaluation indicator for classification.
Option: R-squared
- R-squared is a metric used in regression to describe the proportion of variance in the dependent variable explained by the model. It is not appropriate for evaluating classification models, since classification predicts discrete labels rather than continuous values. Therefore, this option is not an evaluation indicator for classification.
Summary: Among the options, PCA and R-squared are not typical classification evaluation metrics, whereas Precision and Recall are standard and appropriate indicators used to evaluate classification performance.

Which indicators are used for model evaluation in classification? (Select all that apply)

查看解析

登录即可查看完整答案

类似问题

Which of the following are appropriate ways to evaluate the performance of a Support Vector Machine (SVM) used for classification? (Select all that apply)

Which indicators are used for model evaluation in classification? (Select all that apply)

Match the contents to their correct groups. 1: True positive rate (TPR) 2: True negative rate (TNR) 3: Precision 4: Area under the curve (AUC)

Question at position 7 True/False Question: The accuracy metric can distinguish between the various types of misclassification errors a classifier can make. TrueFalse

Suppose you are training a classifier with 2000 data points belonging to a positive class and 250 belonging to a negative class. 1. Which performance metric would be adequate to assess the performance of the model? 2. Which performance metric would be inadequate for this data set?

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单