How to Get the Most From Your Machine Learning Data

Last Updated on June 30, 2020

The data that you use, and how you use it, will likely define the success of your predictive modeling problem.

Data and the framing of your problem may be the point of biggest leverage on your project.

Choosing the wrong data or the wrong framing for your problem may lead to a model with poor performance or, at worst, a model that cannot converge.

It is not possible to analytically calculate what data to use or how to use it, but it is possible to use a trial-and-error process to discover how to best use the data that you have.

In this post, you will discover to get the most from your data on your machine learning project.

After reading this post, you will know:

• The importance of exploring alternate framings of your predictive modeling problem.
• The need to develop a suite of “views” on your input data and to systematically test each.
• The notion that feature selection, engineering, and preparation are ways of creating more views on your problem.

Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code
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