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

## Usage

msle(truth, response, sample_weights = NULL, 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.

sample_weights

(numeric())
Vector of non-negative and finite sample weights. Must have the same length as truth. The vector gets automatically normalized to sum to one. Defaults to equal sample weights.

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)

## Value

Performance value as numeric(1).

## Details

The Mean Squared Log Error is defined as $$\frac{1}{n} \sum_{i=1}^n w_i \left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2.$$

This measure is undefined if any element of $$t$$ or $$r$$ is less than or equal to $$-1$$.

## Meta Information

• Type: "regr"

• Range: $$[0, \infty)$$

• Minimize: TRUE

• Required prediction: response

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

## Examples

set.seed(1)
truth = 1:10
response = truth + rnorm(10)
msle(truth, response)
#> [1] 0.03083585