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The ROC curve is used to compare the performance of classifiers, when they can return a prediction between 0 and 1. A threshold (split the data) at different values, of this probability, can be used to compare the sensitivity with the 1-specificity across different operating conditions of the test. Generally, the best classifier has Select one: а. low true positive rate and a low false positive rate O b. high true positive rate and a low false positive rate c. high true positive rate and a high false positive rate d. high sensitivity and low specificity е. low true positive rate and a high false positive rate

The ROC curve is used to compare the performance of classifiers, when they can return a prediction between 0 and 1. A threshold (split the data) at different values, of this probability, can be used to compare the sensitivity with the 1-specificity across different operating conditions of the test. Generally, the best classifier has Select one: а. low true positive rate and a low false positive rate O b. high true positive rate and a low false positive rate c. high true positive rate and a high false positive rate d. high sensitivity and low specificity е. low true positive rate and a high false positive rate

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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The ROC curve is used to compare the performance of classifiers, when they can return a prediction between 0 and 1. A threshold (split the data) at different values, of this probability, can be used to compare the sensitivity with the 1-specificity across different operating conditions of the test. Generally, the best classifier has Select one: а. low true positive rate and a low false positive rate b. high true positive rate and a low false positive rate c. high true positive rate and a high false positive rate O d. high sensitivity and low specificity е. low true positive rate and a high false positive rate
Transcribed Image Text:The ROC curve is used to compare the performance of classifiers, when they can return a prediction between 0 and 1. A threshold (split the data) at different values, of this probability, can be used to compare the sensitivity with the 1-specificity across different operating conditions of the test. Generally, the best classifier has Select one: а. low true positive rate and a low false positive rate b. high true positive rate and a low false positive rate c. high true positive rate and a high false positive rate O d. high sensitivity and low specificity е. low true positive rate and a high false positive rate
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