rmsle: Root Mean Square Logarithmic Error

In mltools: Machine Learning Tools

Description

Calculate Root-Mean-Square-Logarithmic Error (Deviation)

For the ith sample, Squared Logarithmic Error is calculated as SLE = (log(prediction + 1) - log(actual + 1))^2. RMSLE is then sqrt(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

rmsle(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 Root-Mean-Square-Logarithmic Error (Deviation)

Examples

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

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