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#Boson x leaderboards how to

Top contenders often submit dozens (or hundreds) of models, which increases the odds that one model will over-perform on the publc leaderboard due to random chance of the observations used.

There is significant ‘shakeup’ in leaderboard rankings due primarily to model variance.

The best public score is often a ‘lucky’ fit, and not necessissarily the best model produced by that team.

Public Leaderboard scores and rankings represent the best single response produced by a team.

There is also a strong element of luck involved since the difference between leaderboard scores of the top performers is quite small compared with model variance. Skill is also required in creating a model which produces consitent results when processing new data.

#Boson x leaderboards how to

Significant modeling skill is requried to underatand how to extract the most useful information from this complex data. This analysis indicates that that both skill and luck were significant factors in winning this competition. But I’m in too much pain right now to figure it out. I’m sure there is some insight here in comparing excrutiating tooth pain with statistical analysis. On a personal note, I completed this analyis while waiting for a root canal. This is a non-linear metric which compares the total weight of true positives and penalizes based on the total weight of false positivies. The competition was evaluated using a custom metric called Approximate Median Significance (AMS). There were over 35,000 submissions with many teams testing over 50 submissions. The competition documents state that “The goal of the Higgs Boson Machine Learning Challenge is to explore the potential of advanced machine learning methods to improve the discovery significance of the experiment.” The competition had 1941 individuals participating in 1785 teams. This brief analysis looks at the results of the Higgs Boson Machine Learning Challenge hosted by Kaggle. Our final solution obtained an \emphAMS of 3.71885 on the private leaderboard, making us the top 2% in the Higgs boson challenge.Kaggle Higgs Classification Challenge Leaderboard Analysis Jeff Hebert Saturday, September 20, 2014 Physical meaningful features are further extracted to improve the classification accuracy. Our model learns ensemble of boosted trees that makes careful tradeoff between classification error and model complexity. In this paper, we propose to solve the Higgs boson classification problem with a gradient boosting approach.

The machine learning technique is one important component in solving this problem. A fundamental and challenging task is to extract the signal of Higgs boson from background noises. The next step for physicists is to discover more about the Higgs boson from the data of the Large Hadron Collider (LHC). TI - Higgs Boson Discovery with Boosted TreesīT - Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine LearningĭP - Proceedings of Machine Learning ResearchĪB - The discovery of the Higgs boson is remarkable for its importance in modern Physics research. Our final solution obtained an \emphAMS of 3.71885 on the private leaderboard, making us the top 2% in the Higgs boson challenge. %X The discovery of the Higgs boson is remarkable for its importance in modern Physics research. %C Proceedings of Machine Learning Research %B Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning %T Higgs Boson Discovery with Boosted Trees