How to Selectively Scale Numerical Input Variables for Machine Learning

Last Updated on August 17, 2020

Many machine learning models perform better when input variables are carefully transformed or scaled prior to modeling.

It is convenient, and therefore common, to apply the same data transforms, such as standardization and normalization, equally to all input variables. This can achieve good results on many problems. Nevertheless, better results may be achieved by carefully selecting which data transform to apply to each input variable prior to modeling.

In this tutorial, you will discover how to apply selective scaling of numerical input variables.

After completing this tutorial, you will know:

• How to load and calculate a baseline predictive performance for the diabetes classification dataset.
• How to evaluate modeling pipelines with data transforms applied blindly to all numerical input variables.
• How to evaluate modeling pipelines with selective normalization and standardization applied to subsets of input variables.

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