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Which of the following metrics can be used for evaluating regression models?
These (R Squared, Adjusted R Squared, F Statistics , RMSE / MSE / MAE ) are some metrics which you can use to evaluate your regression model.
What are the two metrics that you can use to evaluate a regression model?
There are three error metrics that are commonly used for evaluating and reporting the performance of a regression model; they are:
- Mean Squared Error (MSE).
- Root Mean Squared Error (RMSE).
- Mean Absolute Error (MAE)
When do you expect regression to the mean?
Regression to the mean, RTM for short, is a statistical phenomenon which occurs when a variable that is in some sense unreliable or unstable is measured on two different occasions. Another way to put it is that RTM is to be expected whenever there is a less than perfect correlation between two measurements of the same thing.
When to use weighted least squares for nonconstant variance?
A plot of the residuals versus the predictor values indicates possible nonconstant variance since there is a very slight “megaphone” pattern: We will turn to weighted least squares to address this possiblity. The weights we will use will be based on regressing the absolute residuals versus the predictor.
Any given variable will usually be measured somewhat imprecisely so that the values obtained do not necessarily correspond to the true underlying values. The extent to which a given observed value of the variable differs from the true value because of measurement error can be referred to as an error score.
Which is a generalization of weighted least squares?
A generalization of weighted least squares is to allow the regression errors to be correlated with one another in addition to having different variances. This leads to generalized least squares, in which various forms of nonconstant variance can be modeled.