APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Adjusted R2 is less useful measure of how well a multiple regression equation fits the sample data than R2
|
|
|
Adjusted R2 increases only whenever your add any new independent variables that do increase the explanatory power of the regression equation
|
|
|
Adjusted R2 always takes on a value between 0 and 1
|
|
|
The closer adjusted R2 is to 1, the better the estimated regression equation fits or explains the relationship between X and Y
|
Detailed explanation-1: -Adjusted R2 is a corrected goodness-of-fit (model accuracy) measure for linear models. It identifies the percentage of variance in the target field that is explained by the input or inputs. R2 tends to optimistically estimate the fit of the linear regression.
Detailed explanation-2: -“R squared” individually can’t tell whether a variable is significant or not because each time when we add a feature, “R squared” can either increase or stay constant. But, it is not true in case of “Adjusted R squared” (increases when features found to be significant).
Detailed explanation-3: -It is defined as the proportion of the variation that can be explained in the dependent variable using the independent variable(s). Therefore, the true statement is: D) R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variable, X .