measures (version 0.3)

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

# NOT RUN {
n = 20
set.seed(123)
truth = abs(rnorm(n))
response = abs(rnorm(n))
MSLE(truth, response)
# }