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Introduction to Boosted Trees

Introduction to Boosted Trees TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAA Tianqi Chen Oct. 22 2014 Outline Review of key concepts of supervised learning regression tree and Ensemble (What are we Learning) Gradient Boosting (How do we Learn) Summary Elements in Supervised Learning Notations: i-th training example Model: how to make prediction given Linear model: (include linear/logistic regression ) The prediction score can have different interpretations depending on the task Linear regression : is the predicted score Logistic regression : is predicted the probability of the instance being positive for example in ranking can be the rank score Parameters: the things we need to learn from data Linear model.

•Model: assuming we have K trees Think: regression tree is a function that maps the attributes to the score •Parameters

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