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.

#### Response variable

The numeric outcome, or response, variable of interest.

#### Predictor

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*).

#### Show results for

If a `filter`

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

data,
`Training`

data, `Test`

data, or `Both`

training and test data. If no filter is active calculations are applied
to all data.

## Example

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.

### Report > Rmd

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 ...")
```

© Vincent Nijs (2019)