How to Use Power Transforms for Machine Learning

published 17.05.2020 21:00


We can see that the shape of the histograms for each variable look more Gaussian than the raw data, much like the box-cox transform.

Below are some common values for lambda The optimal value for this hyperparameter used in the transform for each variable can be stored and reused to transform new data in the future in an identical manner, such as a test dataset or new data in the future.

Power transforms refer to a class of techniques that use a power function (like a logarithm or exponent) to make the probability distribution of a variable Gaussian or more-Gaussian like.

As such, you may be able to achieve better performance on a wide range of machine learning algorithms by transforming input and/or output variables to have a Gaussian or more-Gaussian distribution.

Machine learning algorithms like Linear Regression and Gaussian Naive Bayes assume the numerical variables have a Gaussian probability distribution.

Similar Posts