Tag Archive: machine learning

2017 Sift Engineering in Review

December 31, 2017 4:00 pm Published by

2017 has been a pivotal year for Sift Science and the engineering team.  We’ve delivered on amazing product launches, technological […]

Large Scale Decision Forests: Lessons Learned

August 25, 2015 8:51 pm Published by

At Sift Science, we use a variety of popular machine learning models to detect fraud for our customers. However, until recently we relied exclusively on a combination of linear models and sophisticated feature engineering. As we were reaching the limits of this setup, we began experimenting with our first non-linear model: random decision forests. Several months and over 100 experiments later, we were thrilled to announce the addition of random decision forests to our ensemble of models used to fight fraud. Along the way we learned quite a few things about designing a random decision forest classifier for the fraud detection use case. Here we detail several of these learnings, including how we handled sparse and missing features, useful model visualization techniques, heuristics we used to improve class separation, specialized feature engineering, and how we combined our random decision forest with our existing models. All told, these learnings resulted in an 18% reduction in error for our customers.

Turn Up the Bayes, Part 2

August 12, 2015 5:10 pm Published by

We really love tech talks.At Sift Science, sharing knowledge and facilitating great discussion are two of our favorite things (just behind fraud-fighting, board games, ML, and really beautiful data visualization). In that vein, we've been delighted to host a summer tech talk series entitled Turn Up The Bayes, where we invite awesome engineers to chat about the interesting things that they're working on. To set the mood, we provide delicious pizza and refreshing beverages, and set aside plenty of time for discussion, questions, and more pizza.

Decision Forests: Taking Our Machine Learning to the Next Level

July 9, 2015 5:13 pm Published by

We're adding random decision forests to our machine learning solution, so get ready for an 18% improvement in Sift Score accuracy!This week, we launched an entirely new machine learning model called random decision forests, which will work alongside our existing models. Why? For an additional layer of prediction power, of course. With Sift Science’s decision forests in place, we expect that, on average, our customers will see a significant increase in fraud detection accuracy. This added model makes our online and large-scale learning capabilities even more robust! 

Turn Up The Bayes, Part 1

July 1, 2015 11:46 pm Published by

This week, we hosted the first session of our new summer speaking series (Turn Up The Bayes). I gave a talk on how we leverage a distributed database, HBase, to power an infrastructure that enables performant, distributed online learning. The following is a brief summary...but first, a quick introduction.Fraudsters always search for new ways to exploit opportunities at the expense of companies that provide legitimate goods and services. At Sift Science, we use real-time supervised machine learning to sabotage fraudster plots. As it turns out, the “real-time” portion of our product brings significant infrastructure challenges.

Running ML Infrastructure on HBase

May 29, 2015 6:52 pm Published by

On May 7th, I presented at HBaseCon, demonstrating how Sift Science leverages HBase and its ecosystem in powering our machine learning infrastructure. In case you missed the talk, I’ll lay out the main points here.There are three main types of events that we receive from customers on our platform: page views (also known as page activities), purchases (also known as transactions), and “labels”.

Running ML Infrastructure on HBase

September 23, 2014 12:59 am Published by

We recently hosted our first ever HBase meetup! This was a very exciting event for us as it was the first time we showed off some of the great infrastructure and systems we've built to power our machine learning platform.