You are a data scientist creating a linear regression model. You need to determine how closely the data fits the regression line. Which metric should you review?
MAE is irrelevant to the question. You are being asked which metric measures how closely data fits the regression line. The given answer of R2 is correct.
but you won't know what is 'lower'. i mean you will get a number MAE = 250, it can be a good fit for example R2 = 0,95. If you get an other dataset you get MAE = 5 , but it stuill can be bad fit, it can be R2 = 0,2 . So you cant say about the fit based on only MAE (or RMSE) , but R2 can explain how good is the fit.
The coefficient of determination (R-squared) is the most appropriate metric to determine how closely the data fits the regression line. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
This question is poorly written, no definition of 'fits', I guess normally R2 is think of how fit it is ... but really you need define what fit is in the particular situation, otherwise, A / E could be candidates as well ...
Would say E - MAE directly is using the difference between the prediction and the true value.
RMSE and R2 are both using squared distance for the residual
A model is considered to "fit" the data well if the difference between observed and predicted values is small.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect "fit". However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
The most common interpretation of the coefficient of determination is how well the regression model fits the observed data. For example, a coefficient of determination of 60% shows that 60% of the data fit the regression model. Generally, a higher coefficient indicates a better fit for the model.
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