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*).

If a `filter`

is active (e.g., set in the *Data > View* tab) generate results for `All`

data, `Training`

data, `Validation`

data, or `Both`

training and validation 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 `diamonds`

data. The variables `price`

and `carat`

were log-transformed prior to estimation.The data is available through the *Data > Manage* tab (i.e., choose `Examples`

from the `Load data of type`

drop-down and press `Load`

). The predictions shown below were generated in the *Predict* tab.

The test statistics show a small, but consistent, advantage for the NN.

Add code to *Report > Rmd* to (re)create the analysis by clicking the icon on the bottom left of your screen or by pressing `ALT-enter`

on your keyboard.

`Rsq`

, `RSME`

, and `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 `ggplot2`

commands (see example below)). See *Data > 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*