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 (ANN)*).

#### Show results for

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.

## 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 examples`

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

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

### R > Report

Add code to *R > Report* 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 *R > Report*. 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 (2017)