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Peter Prettenhofer - Gradient Boosted Regression Trees in scikit-learn
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PyData SV 2014
Gradient Boosted Regression Trees (GBRT) is powerful a statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling -- it is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price. I will start with a brief introduction to the GBRT model -- focusing on intuition rather than mathematical formulas. The majority of the tutorial will be dedicated to an in depth discussion how to apply GBRT successfully in practice using scikit-learn. We will cover important topics such as regularization, model tuning, and model interpretation that should significantly improve your score on Kaggle. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Gradient Boosted Regression Trees (GBRT) is powerful a statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling -- it is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price. I will start with a brief introduction to the GBRT model -- focusing on intuition rather than mathematical formulas. The majority of the tutorial will be dedicated to an in depth discussion how to apply GBRT successfully in practice using scikit-learn. We will cover important topics such as regularization, model tuning, and model interpretation that should significantly improve your score on Kaggle. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
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