On his journey to the top spot at Kaggle, Marios Michailidis noticed that many of the things he does to perform competitively in data challenges could be automated. In fact, automation is critical to achieve very predictive scores because while the machine “runs,” he can focus on other aspects of the modeling process to extract the most value.
Marios shares lessons learned from his Kaggle experience and shows how you can achieve competitive performance in predictive modeling tasks automatically, using H2O.ai’s Driverless AI—an AI that creates AI. Along the way, you’ll explore the basic elements of stacking and StackNet, a generalized framework that uses it.
Marios Michailidis is a research data scientist at H2O.ai and a recent world no.1 Kaggle Grandmaster. He has worked in the marketing and credit sectors in the UK market and has successfully led multiple analytics projects for acquisition, retention, recommenders, uplift, fraud detection, portfolio optimization, and more. Previously, he was senior personalization data scientist at dunnhumby, where his main role was to improve existing algorithms and the research benefits of advanced machine learning methods and provide data insights. He created a matrix factorization library in Java along with a demo version of personalized search capability. He also held positions of importance at iQor, Capita, British Pearl, and Ey-Zein. Marios is the creator and administrator of KazAnova, a freeware GUI for quick credit scoring and data mining, which was built entirely in Java. He is also the creator of the StackNet Meta-Modeling Framework. His hobbies include competing in predictive modeling competitions. He was recently ranked first out of 465,000 data scientists on the popular data competition platform Kaggle. Marios is finishing his PhD in machine learning at the University College London (UCL) with a focus on ensemble modeling. He holds a BSc in accounting finance from the University of Macedonia in Greece and an MSc in risk management from the University of Southampton.
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