Before we dive into defining feature selection for machine learning, we must first understand what a feature represents.
A feature is a characteristic or measurable property of what the machine learning model is attempting to analyze or predict. Features appear as columns in a dataset, and adding or removing features can help improve the accuracy of your analysis.
Feature selection involves adjusting which variables are included in the dataset as needed – the goal is to utilize only the variables that will help your organization solve the problem at hand.
Adding additional features can improve your machine learning model when it is too simple to fit the existing data. On the other hand, eliminating certain features helps fine-tune the model blueprints, avoiding focusing on variables that do not contribute to anything.
Think of a model that is trying to predict how likely someone is to make a purchase – the number of items in their cart may help the model predict this, but their email address will not.
There are a few ways to perform this critical process, including filter-based, wrapper-based, and embedded feature selection. Filter-based feature selection filters out features based on a specific metric like correlation. Wrapper-based methods view feature selection as a search problem and embedded methods used algorithms with built-in feature selection options like Lasso and RF.
Feature selection is an essential aspect of data science and the creation of machine learning algorithms.
This process reduces the chance of overfitting, where the model is trained on a dataset that is too specific and cannot make accurate predictions with new or broader information. Feature selection also makes machine learning models more interpretable, since a larger number of features make the algorithm challenging to explain.
Appropriate feature selection also allows models to learn faster since there is less data for the software to analyze and interpret. Likewise, the term garbage-in, garbage-out applies here – if the data used to train the machine learning model is not clean, the outputs will be low-quality.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage feature selection or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from feature selection.
LogicPlum’s automated machine learning platform allows you to accurately and efficiently perform feature selection. Our goal is to help you build algorithms that will enable you to gain valuable insights about your business, including reports like feature impact.
We provide unique model blueprints that will automatically perform feature selection for you, and you can even test different subsets of features to see how they compare.
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