We started Feature Labs with a straightforward goal: build products that enable any organization to create and deploy machine learning solutions. In 2015 while working in the MIT Computer Science and Artificial Intelligence Lab, my co-founders and I developed an...read more
The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical...read more
All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap, these two ideas have different objectives. Knowing...read more
Prior to starting Feature Labs, I researched data science automation in the Data to AI Lab at MIT. Unlike most data scientists who work in a single domain, our group had sponsors from a wide range of industries. This gave us the unique opportunity to develop innovative solutions to use with the diverse problems we worked on.read more
Feature Labs is pleased to share that our open source library, Featuretools, is being used in a new MIT course on Data Science and Big Data Analytics. Feature engineering is a vital skill for all data scientists, so we are excited to provide the library that enables teaching it alongside other important machine learning topics for the first time.read more
If you use a credit card, you probably know the feeling of having your card declined due to a suspected fraudulent transaction. An industry report from 2015 found that one out of every six legitimate cardholders experienced at least one declined transaction because of inaccurate fraud detection in the past year. That makes fraud detection an expensive problem for issuers: Those declined transactions lead to nearly $118 billion dollars in losses on an annual basis.
Even though numerous machine learning approaches have been developed in the past to address fraud, newly introduced data science automation platforms like Feature Labs give us a reason to revisit the problem. And now, any organization can see the power of automation for themselves using our just announced developer library, Featuretools.read more
Deep Feature Synthesis: How Automated Feature Engineering Works January 16, 2018
Feature Engineering: The Secret to Data Science Success November 14, 2017
Applying Data Science Automation to Better Predict Credit Card Fraud October 25, 2017
Open Sourcing Featuretools September 27, 2017
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Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and proprietary algorithms for automated feature engineering. Contact us to learn how we can help you succeed with data science and predictive modeling endeavors.
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Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and proprietary algorithms for automated feature engineering.