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

## Details

R Squared is defined as $$
1 - \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}.
$$
Also known as coefficient of determination or explained variation.
Subtracts the `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 \(t\).

## Note

The score function calls `mlr3measures::rsq()`

from package mlr3measures.

If the measure is undefined for the input, `NaN`

is returned.
This can be customized by setting the field `na_value`

.

## Dictionary

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

:

```
$get("regr.rsq")
mlr_measuresmsr("regr.rsq")
```

## See also

Dictionary of Measures: mlr_measures

`as.data.table(mlr_measures)`

for a complete table of all (also dynamically created) Measure implementations.

Other regression measures:
`mlr_measures_regr.bias`

,
`mlr_measures_regr.ktau`

,
`mlr_measures_regr.mae`

,
`mlr_measures_regr.mape`

,
`mlr_measures_regr.maxae`

,
`mlr_measures_regr.medae`

,
`mlr_measures_regr.medse`

,
`mlr_measures_regr.mse`

,
`mlr_measures_regr.msle`

,
`mlr_measures_regr.pbias`

,
`mlr_measures_regr.rae`

,
`mlr_measures_regr.rmse`

,
`mlr_measures_regr.rmsle`

,
`mlr_measures_regr.rrse`

,
`mlr_measures_regr.rse`

,
`mlr_measures_regr.sae`

,
`mlr_measures_regr.smape`

,
`mlr_measures_regr.srho`

,
`mlr_measures_regr.sse`