Measure to compare true observed response with predicted response in regression tasks.

## Details

R Squared is defined as $$ 1 - \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}, $$ where \(\bar{t} = \sum_{i=1}^n t_i\).

Also known as coefficient of determination or explained variation.
Subtracts the `mlr3measures::rse()`

from 1, hence it compares the squared error of the predictions relative to a naive model predicting the mean.

This measure is undefined for constant \(t\).

## Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function `msr()`

:

## Meta Information

Task type: “regr”

Range: \((-\infty, 1]\)

Minimize: FALSE

Average: macro

Required Prediction: “response”

Required Packages: mlr3

## See also

Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval

Package mlr3measures for the scoring functions. Dictionary of Measures: mlr_measures

`as.data.table(mlr_measures)`

for a table of available Measures in the running session (depending on the loaded packages).Extension packages for additional task types:

mlr3proba for probabilistic supervised regression and survival analysis.

mlr3cluster for unsupervised clustering.

Other Measure:
`Measure`

,
`MeasureClassif`

,
`MeasureRegr`

,
`MeasureSimilarity`

,
`mlr_measures`

,
`mlr_measures_aic`

,
`mlr_measures_bic`

,
`mlr_measures_classif.costs`

,
`mlr_measures_debug_classif`

,
`mlr_measures_elapsed_time`

,
`mlr_measures_internal_valid_score`

,
`mlr_measures_oob_error`

,
`mlr_measures_selected_features`

## Super classes

`mlr3::Measure`

-> `mlr3::MeasureRegr`

-> `MeasureRSQ`

## Methods

## Inherited methods

### Method `new()`

Creates a new instance of this R6 class.

#### Usage

`MeasureRegrRSQ$new(pred_set_mean = TRUE)`