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陈天奇ppt

这是陈天奇ppt,包括了Review of key concepts of supervised learning,Regression Tree and Ensemble (What are we Learning),Gradient Boosting (How do we Learn),Summary等内容,欢迎点击下载。

陈天奇ppt是由红软PPT免费下载网推荐的一款课件PPT类型的PowerPoint.

Outline Elements in Supervised Learning Elements continued: Objective Function Putting known knowledge into context Objective and Bias Variance Trade-off Outline Regression Tree (CART) Regression Tree Ensemble Tree Ensemble methods Put into context: Model and Parameters Learning a tree on single variable Learning a step function Learning step function (visually) Coming back: Objective for Tree Ensemble Objective vs Heuristic Regression Tree is not just for regression! Outline Take Home Message for this section So How do we Learn? Additive Training Taylor Expansion Approximation of Loss Our New Goal Refine the definition of tree Define Complexity of a Tree (cont’) Revisit the Objectives The Structure Score The Structure Score Calculation Searching Algorithm for Single Tree Greedy Learning of the Tree Efficient Finding of the Best Split An Algorithm for Split Finding What about Categorical Variables? Pruning and Regularization Recap: Boosted Tree Algorithm Outline Questions to check if you really get it Questions to check if you really get it Questions to check if you really get it Summary Reference

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