Calculates the per-observation squared error as $$
\left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2.
$$

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

Note that this is an unaggregated measure, returning the losses per observation.

## Usage

`sle(truth, response, ...)`

## 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`

.

- ...
(`any`

)

Additional arguments. Currently ignored.

## Value

Performance value as `numeric(length(truth))`

.

Type: `"regr"`

Range (per observation): \([0, \infty)\)

Minimize (per observation): `TRUE`

Required prediction: `response`

## See also

Other Regression Measures:
`ae()`

,
`ape()`

,
`bias()`

,
`ktau()`

,
`linex()`

,
`mae()`

,
`mape()`

,
`maxae()`

,
`maxse()`

,
`medae()`

,
`medse()`

,
`mse()`

,
`msle()`

,
`pbias()`

,
`pinball()`

,
`rae()`

,
`rmse()`

,
`rmsle()`

,
`rrse()`

,
`rse()`

,
`rsq()`

,
`sae()`

,
`se()`

,
`smape()`

,
`srho()`

,
`sse()`