# Ensemble Learning

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## Boosting

Reference Foundations of Machine Learning Chapter 6
Idea: Build a strong learner from a set of weak learners.

Learn a linear combination of our weak learners.

Given a sample of size m
for i=1:m
d_i=1/m
for t=1:T
h_t <- classifier
alpha_t <- (1/2)log((1-eps_t)/eps_t)
z_t <- e[eps_t(1-eps_t)]^(1/2)
for i=1:m
D_{t+1} <- (D_t(i)exp(-alpha_t*y_i*h_t(x_i))/z_t
g <- sum alpha_t h_t


## Bagging

Bagging Predictors
Bootstrap aggregation
Idea: Given a sample S, bootstrap from the sample to get m samples S_1,...,S_m.
Then build m classifers from those samples
Your new classifier is a linear combination of those classifiers