measures: MSLE – R documentation

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MSLE

Mean squared logarithmic error

Description

Defined as: mean((log(response + 1, exp(1)) - log(truth + 1, exp(1)))^2). This is mostly used for count data, note that all predicted and actual target values must be greater or equal '-1' to compute the mean squared logarithmic error.

Usage

MSLE(truth, response)

Arguments

`truth`	[numeric] vector of true values
`response`	[numeric] vector of predicted values

Examples

n = 20
set.seed(123)
truth = abs(rnorm(n))
response = abs(rnorm(n))
MSLE(truth, response)

measures

Performance Measures for Statistical Learning

v0.3

GPL-3

Authors

Philipp Probst [aut, cre]

Initial release