#1. Learning-To-Rank algorithm is renowned for solving ranking problems in text retrieval, however it is also possible to apply the algorithm into non-text data-sets such as player leaderboard. Joining us today in the 14th edition of the Kaggle Grandmaster Series is one of the youngest Kaggle Grandmasters- Peiyuan Liao. “A majority of books or courses are based on overly used datasets or benchmarks but things get harder as you face real-world noisy problems.” For this week’s ML practitioner’s series, we got in touch with Oliver Grellier — 2x Kaggle GM and a senior data scientist at H2O.ai, a leading open-source machine learning and artificial intelligence platform trusted by data … English V. #001. Peiyuan is the youngest Chinese Kaggle Competitions Grandmaster and ranks 28th with 7 gold medals to his name. Jeremy Achin, cofounder of startup DataRobot, which competes with H2O and also has hired grandmasters, says high Kaggle rankings also help weed out poseurs trying to exploit the data-skills shortage. In other words, the goal was simply to be as accurate as possible. He is also a Kaggle Discussions Master and an Expert in the Kaggle Notebooks section. Worse, Kaggle literally solves most of the problems for you. I was very excited and was really enchanted by everything I was learning, and after only a few months, I won my first prize in Kiva’s Kernels Data Science for Good, taking a thousand-dollar prize. What’s interesting, though, is that we’re relatively new to Kaggle … Rankings on Kaggle, although a great accomplishment, will not be enough unless candidate’s have one (or more) of the selling points mentioned above. If you’ve been keeping up with the Kaggle News, you may be familiar with the Mechanisms of Action competition by the Laboratory for Innovation Science at Harvard recently closed. Unlike pure classification use cases where you are right or wrong, in a ranking problem… I would definitely participate in more challenges. Kaggle Grandmaster Series – Exclusive Interview with Kaggle Competitions Grandmaster Philip Margolis (#Rank 47) January 25, 2021 “I just followed my interests and focussed on learning machine learning as much as I could”- Philip Margolis Kaggle, a popular platform for data science competitions, can be intimidating for beginners to get into.. After all, some of the listed competitions have over $1,000,000 prize pools and hundreds of competitors. This is my first Kaggle challenge experience and I was quite delighted with this result. The Discounted Cumulative Gain (DCG) is a relevance metric in information science and information retrieval. Kaggle’s Problem Housing Price. Rankings. Theo’s Kaggle Journey from Scratch to become a Kaggle Grandmaster. If their limited to nonexistent real-world relevance wasn’t enough, Kaggle contests put data scientists in the rat-race. Top teams boast decades of combined experience, tackling ambitious problems such as improving airport security or analyzing satellite data. The Kaggle leaderboard measured the macro F1 score of the job predictions. ... learning process and for me it is one of the keys to making the model more accurate and scratching places in the ranking. I’m proud to say that my partner, Andy Wang, and I managed to place in the top 4% — 152nd out of 4,373 teams. My first interaction with Kaggle was on January 8, 2018 and in a month and a half, I reached the level of Kaggle Expert Kernels. I then took the 10 highest performers on the private leaderboard and measured their macro disparate impact to establish the fairness rankings. 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