Getting the Most from your Data

Once more, Jason Brownlee makes excellent points.   It starts and ends with the data.   Its the biggest asset, and also the largest obstacle.  Frame it and test it.  Is there enough, does it support the methods you can use,  is it clean?  Here is his intro, much more at the link:


How to Get the Most From Your Machine Learning Data
by Jason Brownlee on April 16, 2018 in Machine Learning Process

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.

Let’s get started.... "