Mean Squared Log Error

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)
Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Details

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

See also

Other Regression Measures: ae(), ape(), bias(), ktau(), linex(), mae(), mape(), maxae(), maxse(), medae(), medse(), mse(), pbias(), pinball(), 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