Evaluate regression model performance
To download the table as a csv-files click the top download button on the right of your screen. To download the plots at a png file click the lower download icon on the right of your screen.
The numeric outcome, or response, variable of interest.
Select one or more variables that can be used to predict the value of the response variable. This could be a variable or predicted values from a model (e.g., from a regression estimated using Model > Linear regression (OLS) or a Neural Network estimated using Model > Neural Network).
filter is active (e.g., set in the Data >
View tab) generate results for
Test data, or
training and test data. If no filter is active calculations are applied
to all data.
Predictions were derived from a linear regression and an neural
network with two nodes in the hidden layer on the
data. The variables
log-transformed prior to estimation.The data is available through the
Data > Manage tab (i.e., choose
Load data of type drop-down and press
Load). The predictions shown below were generated in the
The test statistics show a small, but consistent, advantage for the NN.
Add code to
> Rmd to (re)create the analysis by clicking the
icon on the bottom
left of your screen or by pressing
ALT-enter on your
MAE are plotted
by default. It is possible to customize the plotted results through
Report > Rmd. To change the plot use, for example:
plot(result, vars = "Rsq")
The plot can be further customized using
commands (see example below)). See
> Visualize for details.
plot(result, vars = "Rsq") + labs(caption = "Based on data from ...")
For an overview of related R-functions used by Radiant to evaluate regression models see Model > Evaluate regression