msle: Mean Square Logarithmic Error

In mltools: Machine Learning Tools

Description

Calculate Mean-Square-Logarithmic Error (Deviation)

For the ith sample, Squared Logarithmic Error is calculated as SLE = (log(prediction + 1) - log(actual + 1))^2. MSE is then mean(squared logarithmic errors). Note the '+1' in the calculation of SLE which avoids taking the logarithm of 0 for data which may include 0s.

Usage

msle(preds = NULL, actuals = NULL, weights = 1, na.rm = FALSE)

Arguments

preds	A vector of prediction values in [0, 1]
actuals	A vector of actuals values in 0, 1, or FALSE, TRUE
weights	Optional vectors of weights
na.rm	Should (prediction, actual) pairs with at least one NA value be ignored?

Details

Calculate Mean-Square-Logarithmic Error (Deviation)

Examples

preds <- c(1.0, 2.0, 9.5)
actuals <- c(0.9, 2.1, 10.0)
msle(preds, actuals)

mltools documentation built on May 2, 2019, 5:22 a.m.