recitation-final-hanghang

recitation-final-hanghang - Recitation 10-701/15-781, Fall...

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Recitation 10-701/15-781, Fall 2008 Final Review By Hanghang Tong (htong@cs) Some Parts of the slides are from previous years’ recitation and lecture notes 1
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Chaptering Classifiers (binary case) How many classifiers have we learned in class? Gaussian Bayes Naïve Bayes Logistic Regression K-NN SVM ANN Ada-Boost Decision Tree Q: How do we chapter them ? ( = how can we remember them ? ) 2
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Category I: Model r(x) Observation: classification is a special case of regression Goal: to model r(x) = E(P(Y|X)) = P(Y=1|X) Choice I: Linear Regression r(x) = w’ X +b This model is wrong (why?)! Still useful somehow in practice Choice II: Logistic Regression Choice III: Generalized Linear Models (not covered in class) G (r (x)) = w’X In the case of LR 3 0 1 ( ) ( 1| , ) 1 exp( ( )) ii i r x P Y X w w w X 1 ( ) log( ) () T rx wx
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Cateogry II: Bayes Classifier Observation: #1: Bayes Classifier is optimal in theory! #2: Goal: to estimate P(X|Y=1), P(X|Y=0), P(Y=1), P(Y=0)
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This note was uploaded on 01/26/2010 for the course MACHINE LE 10701 taught by Professor Ericp.xing during the Fall '08 term at Carnegie Mellon.

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recitation-final-hanghang - Recitation 10-701/15-781, Fall...

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