Match the definitions to the correct regression evaluation metrics:Tells us the proportion of variation in Y explained by X. Caution needed since it can be artificially inflated by adding too many input variablesThis is not a regression evaluation metricTells us the proportion of variation in Y explained by X. Additionally, it punishes the inclusion of useless input variables, and rewards those with better predictive powerThis is not a regression evaluation metricF1 scoreR2AccuracyAdjusted R2

The three common evaluation metrics for regression are

Which two of the of the following statements are correct regarding R2 as a tool to evaluate a regression model. Multiple answers allowed.a.The R2 is the proportion of the total variation in X that can be explained by the variation in the dependent variable in the model.b. If the R2 value is greater than 1, then it shows that the regression model is a very good fit.c.The R2 value needs to be reported in presenting regression results as it shows the relative precision of the model for the users to consider.d.R2 reflects the model’s improvement of the predicted value over the estimator of the sample mean (Y-bar).

In regression analysis, r2 is the ___.Group of answer choicescoefficient of determinationsum of the squaredestimated regression analysiscoefficient of correlation

The regression R2 is: a. possible to decrease when an additional explanatory variable is added. b. R S S divided by T S S. c. a measure of the goodness of fit of your regression line. d. a measure of the causal effect of X on Y.

Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?(1 Point)AUC-ROCAccuracyLoglossMean-Squared-Error