How to Grid Search Data Preparation Techniques

Last Updated on August 17, 2020

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling.

The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise.

An alternative approach to data preparation is to grid search a suite of common and commonly useful data preparation techniques to the raw data. This is an alternative philosophy for data preparation that treats data transforms as another hyperparameter of the modeling pipeline to be searched and tuned.

This approach requires less expertise than the traditional manual approach to data preparation, although it is computationally costly. The benefit is that it can aid in the discovery of non-intuitive data preparation solutions that achieve good or best performance for a given predictive modeling problem.

In this tutorial, you will discover how to use the grid search approach for data preparation with tabular
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