AI engineering glossary · Evaluation metrics
What is MSLE (mean squared logarithmic error)?
Mean squared logarithmic error (also called MSLE): The mean of squared differences between the natural logs of (1 + prediction) and (1 + actual). It measures relative rather than absolute error, so it treats being off by a factor as equally bad at small and large scales. It suits targets spanning orders of magnitude, but needs non-negative values.
Mean squared logarithmic error: an example
Predicting 200 for an actual 100 gives a squared log error of about 0.47; predicting 1,100 for an actual 1,000 gives only about 0.009.
Related terms
- Root mean squared logarithmic error
- Mean squared error
- Mean absolute percentage error
- Root mean squared error
More Evaluation metrics terms
- Median absolute error
- METEOR
- Micro averaging
- Negative predictive value
- Normalised discounted cumulative gain
- Normalised mutual information
- Normalised RMSE
- Operating point
Where you learn it
Mean squared logarithmic error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.