msle: Mean Squared Log Error

In mfrasco/Metrics: Evaluation Metrics for Machine Learning

Description

msle computes the average of squared log error between two numeric vectors.

Usage

msle(actual, predicted)

Arguments

actual	The ground truth non-negative vector
predicted	The predicted non-negative vector, where each element in the vector is a prediction for the corresponding element in actual.

Details

msle adds one to both actual and predicted before taking the natural logarithm to avoid taking the natural log of zero. As a result, the function can be used if actual or predicted have zero-valued elements. But this function is not appropriate if either are negative valued.

See Also

rmsle sle

Examples

actual <- c(1.1, 1.9, 3.0, 4.4, 5.0, 5.6)
predicted <- c(0.9, 1.8, 2.5, 4.5, 5.0, 6.2)
msle(actual, predicted)

mfrasco/Metrics documentation built on May 13, 2019, 12:31 a.m.