sle: Squared Log Error

In Metrics: Evaluation Metrics for Machine Learning

Description

sle computes the elementwise squares of the differences in the logs of two numeric vectors.

Usage

sle(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

sle adds one to both actual and predicted before taking the natural logarithm of each 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

msle rmsle

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)
sle(actual, predicted)

Metrics documentation built on May 1, 2019, 10:11 p.m.