MSLE: Mean squared logarithmic error

In measures: Performance Measures for Statistical Learning

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 documentation built on Jan. 19, 2021, 5:08 p.m.