To measure the performance of a regression model, we can calculate the distance from predicted output and the actual output as a quantifier of the performance of the model. Here, we often use the root mean square error (RMSE), relative square error (RSE) and R-Square as common measurements. In the following recipe, we will illustrate how to compute these measurements from a built regression model.
In this recipe, we will use the
Quartet dataset, which contains four regression datasets, as our input data source.
Perform the following steps to measure the performance of the regression model:
Quartetdataset from the
> library(car) > data(Quartet)