D. All of the mentioned<\/strong><\/p>\nR-squared, Kappa, and RMSE are all statistical measures that are used to evaluate the performance of a model. However, they each measure different things.<\/p>\n
R-squared is a measure of the proportion of the variance in the dependent variable that is explained by the model. It is a number between 0 and 1, where 0 indicates that the model does not explain any of the variance in the dependent variable and 1 indicates that the model perfectly explains the variance in the dependent variable.<\/p>\n
Kappa is a measure of the agreement between two sets of data. It is a number between -1 and 1, where -1 indicates that there is perfect disagreement between the two sets of data and 1 indicates that there is perfect agreement between the two sets of data.<\/p>\n
RMSE is a measure of the root mean squared error of a model. It is a number that indicates how much the model’s predictions deviate from the actual values.<\/p>\n
In the figure, R-squared, Kappa, and RMSE are all shown to be increasing as the number of features increases. However, this is not always the case. For example, if the model is overfitting the data, then R-squared, Kappa, and RMSE may all decrease as the number of features increases.<\/p>\n
Therefore, the correct answer is: D. All of the mentioned<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"[amp_mcq option1=”R Squared” option2=”Kappa” option3=”RMSE” option4=”All of the mentioned” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[738],"tags":[],"class_list":["post-47740","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n
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