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

Question

BlackTom AI · Accepted Answer

To begin, consider the task: identifying indicators used for evaluating classification models.
Option 1: PCA. This stands for Principal Component Analysis, which is a dimensionality reduction technique, not an evaluation metric. While PCA can be used in preprocessing or visualization, it does not measure classification performance, so this option is not appropriate as an evaluation indicator.
Option 2: Precision. Precision is defined as the ratio of true positives to the sum of true positives and false positives. It directly reflects how trustworthy the positive predictions are in a classification task, making it a valid evaluation metric for classification models.
Option 3: Recall. Recall, also known as sensitivity or true positive rate, is the ratio of true positives to the sum of true positives and false negatives. It measures how many actual positive instances the model correctly identifies, and it is a standard metric for classification performance.
Option 4: R-squared. R-squared is a statistic used to assess goodness-of-fit for regression models, indicating how well the variance of the outcome is explained by the model. It is not applicable as an evaluation metric for classification tasks.
In summary, among the given options, Precision and Recall are the appropriate indicators for evaluating classification models, while PCA and R-squared do not serve as standard classification evaluation metrics.

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

View Explanation

Log in for full answers

Similar Questions

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?

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