Regression measure defined as $$
\frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}.
$$
Can be interpreted as squared error of the predictions relative to a naive model predicting the mean.

rse(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. |

## Value

Performance value as `numeric(1)`

.

## Note

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

## See also

Other Regression Measures:
`bias()`

,
`ktau()`

,
`mae()`

,
`mape()`

,
`maxae()`

,
`maxse()`

,
`medae()`

,
`medse()`

,
`mse()`

,
`msle()`

,
`pbias()`

,
`rae()`

,
`rmse()`

,
`rmsle()`

,
`rrse()`

,
`rsq()`

,
`sae()`

,
`smape()`

,
`srho()`

,
`sse()`

## Examples

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