Feature engineering is a critical part of the machine learning process. Typically, it’s a time-consuming manual effort to transform and aggregate data to maximize your model’s predictive power.
But what if you could construct new features automatically, building better models, saving time, and avoiding the mistakes that can occur in a manual process?
In this video from our Virtual Global Inspire conference, I explain what feature engineering is and how it fits in your ML process. I also demonstrate how you can use Featuretools from Alteryx Open Source to automate your feature engineering efforts and ideally obtain higher performance from your models in less time.
I hope the video has encouraged you to give Featuretools a try! And, if you’re not into Python, note that Featuretools’ automated feature engineering capabilities are also part of the Alteryx Intelligence Suite and the new Alteryx Machine Learning as well. Whichever tool you use, we hope you’ll enjoy how Featuretools makes successful feature engineering easier.
What questions do you have about feature engineering or Featuretools? Which other tools or data science concepts would you like to see addressed here on the blog? Let us know with a comment, and be sure to subscribe to the blog to get future articles.
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