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sle

Squared Log Error


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

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

Evaluation Metrics for Machine Learning

v0.1.4
BSD_3_clause + file LICENSE
Authors
Ben Hamner [aut, cph], Michael Frasco [aut, cre], Erin LeDell [ctb]
Initial release

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