Feature Selection to Improve Accuracy and Decrease Training Time

Last Updated on August 16, 2020

Working on a problem, you are always looking to get the most out of the data that you have available. You want the best accuracy you can get.

Typically, the biggest wins are in better understanding the problem you are solving. This is why I stress you spend so much time up front defining your problem, analyzing the data, and preparing datasets for your models.

A key part of data preparation is creating transforms of the dataset such as rescaled attribute values and attributes decomposed into their constituent parts, all with the intention of exposing more and useful structure to the modeling algorithms.

An important suite of methods to employ when preparing the dataset are automatic feature selection algorithms. In this post you will discover feature selection, the benefits of simple feature selection and how to make best use of these algorithms in Weka on your dataset.

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