# rsq: R Squared In mlr3measures: Performance Measures for 'mlr3'

 rsq R Documentation

## R Squared

### Description

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

### Usage

```rsq(truth, response, na_value = NaN, ...)
```

### Arguments

 `truth` (`numeric()`) True (observed) values. Must have the same length as `response`. `response` (`numeric()`) Predicted response values. Must have the same length as `truth`. `na_value` (`numeric(1)`) Value that should be returned if the measure is not defined for the input (as described in the note). Default is `NaN`. `...` (`any`) Additional arguments. Currently ignored.

### Details

R Squared is defined as

1 - sum((t - r)^2) / sum((t - mean(t))^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.

### Value

Performance value as `numeric(1)`.

### Meta Information

• Type: `"regr"`

• Range: (-Inf, 1]

• Minimize: `FALSE`

• Required prediction: `response`

Other Regression Measures: `ae()`, `ape()`, `bias()`, `ktau()`, `mae()`, `mape()`, `maxae()`, `maxse()`, `medae()`, `medse()`, `mse()`, `msle()`, `pbias()`, `rae()`, `rmse()`, `rmsle()`, `rrse()`, `rse()`, `sae()`, `se()`, `sle()`, `smape()`, `srho()`, `sse()`

### Examples

```set.seed(1)
truth = 1:10
response = truth + rnorm(10)
rsq(truth, response)
```

mlr3measures documentation built on Aug. 5, 2022, 5:22 p.m.