AI News, What machine learning approaches have won most Kaggle competitions?

What machine learning approaches have won most Kaggle competitions?

If by approaches you mean models, then Gradient Boosting is by far the most successful single model.

In my very first Kaggle competition, 3 years ago, a fairly simple average of models was enough to win a competition.

That has a lot to do with the simple reality of more people = more ideas and more work hours, but also the simple reality of larger ensembles being beneficial.

The new Kaggle Script platform make it very easy to get up to speed and there is little reason to spend 3 months on a competition when you can spend only 1.

Stacked Ensemble Models and Data Science Competitions

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Modeling Ensembles using the Caret Package: Machine Learning With R

Support these videos, check out my in-depth classes on Udemy.com (discounts and specials) at Simple way to run ..

Stacked Ensemble Models and Data Science Competitions

Author: Funda Günes, SAS Institute Inc. More on KDD2017 Conference is published on

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Artur Fattakhov, Ilya Kibardin and Dmitriy Abulkhanov share their winner's solutions of Kaggle Camera Model Identification. In this competition, Kagglers ...

Predicting the Winning Team with Machine Learning

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Kaggle Carvana Image Masking: определение фона на изображениях автомобилей — Сергей Мушинский

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Building Stacking Ensemble in R using six algorithms - Arabic

Building Stacking Ensemble in R using six different machine learning algorithms: SVM - Naive Bayes - Recursive Partitioning Trees - Linear Discriminant ...