Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline. Unnecessary features decrease training speed, decrease model interpretability, and, most importantly, decrease generalization performance on the test set. Frustrated by the ad-hoc feature selection methods I found myself applying over and over again for machine learning problems, I built a class for feature selection in Python available on GitHub. In this article we will walk through using the FeatureSelector on an example machine learning dataset. We'll see how it allows us to rapidly implement these methods, allowing for a more efficient workflow. The complete code is available on GitHub and I encourage any contributions.

Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline. Unnecessary features decrease training speed, decrease model interpretability, and, most importantly, decrease generalization performance on the test set. Frustrated by the ad-hoc feature selection methods I found myself applying over and over again for machine learning problems, I built a class for feature selection in Python available on GitHub. In this article we will walk through using the FeatureSelector on an example machine learning dataset. We'll see how it allows us to rapidly implement these methods, allowing for a more efficient workflow.