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<!DOCTYPE html>
<html>
<head>
<title>Bagging</title>
<meta charset="utf-8">
<meta name="description" content="Bagging">
<meta name="author" content="Jeffrey Leek">
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</head>
<body style="opacity: 0">
<slides class="layout-widescreen">
<!-- LOGO SLIDE -->
<slide class="title-slide segue nobackground">
<aside class="gdbar">
<img src="../../assets/img/bloomberg_shield.png">
</aside>
<hgroup class="auto-fadein">
<h1>Bagging</h1>
<h2></h2>
<p>Jeffrey Leek<br/>Johns Hopkins Bloomberg School of Public Health</p>
</hgroup>
<article></article>
</slide>
<!-- SLIDES -->
<slide class="" id="slide-1" style="background:;">
<hgroup>
<h2>Bootstrap aggregating (bagging)</h2>
</hgroup>
<article data-timings="">
<p><strong>Basic idea</strong>: </p>
<ol>
<li>Resample cases and recalculate predictions</li>
<li>Average or majority vote</li>
</ol>
<p><strong>Notes</strong>:</p>
<ul>
<li>Similar bias </li>
<li>Reduced variance</li>
<li>More useful for non-linear functions</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-2" style="background:;">
<hgroup>
<h2>Ozone data</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">library(ElemStatLearn); data(ozone,package="ElemStatLearn")
ozone <- ozone[order(ozone$ozone),]
head(ozone)
</code></pre>
<pre><code> ozone radiation temperature wind
17 1 8 59 9.7
19 4 25 61 9.7
14 6 78 57 18.4
45 7 48 80 14.3
106 7 49 69 10.3
7 8 19 61 20.1
</code></pre>
<p><a href="http://en.wikipedia.org/wiki/Bootstrap_aggregating">http://en.wikipedia.org/wiki/Bootstrap_aggregating</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-3" style="background:;">
<hgroup>
<h2>Bagged loess</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">ll <- matrix(NA,nrow=10,ncol=155)
for(i in 1:10){
ss <- sample(1:dim(ozone)[1],replace=T)
ozone0 <- ozone[ss,]; ozone0 <- ozone0[order(ozone0$ozone),]
loess0 <- loess(temperature ~ ozone,data=ozone0,span=0.2)
ll[i,] <- predict(loess0,newdata=data.frame(ozone=1:155))
}
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-4" style="background:;">
<hgroup>
<h2>Bagged loess</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">plot(ozone$ozone,ozone$temperature,pch=19,cex=0.5)
for(i in 1:10){lines(1:155,ll[i,],col="grey",lwd=2)}
lines(1:155,apply(ll,2,mean),col="red",lwd=2)
</code></pre>
<div class="rimage center"><img src="fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" class="plot" /></div>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-5" style="background:;">
<hgroup>
<h2>Bagging in caret</h2>
</hgroup>
<article data-timings="">
<ul>
<li>Some models perform bagging for you, in <code>train</code> function consider <code>method</code> options
<ul>
<li><code>bagEarth</code> </li>
<li><code>treebag</code></li>
<li><code>bagFDA</code></li>
</ul></li>
<li>Alternatively you can bag any model you choose using the <code>bag</code> function</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-6" style="background:;">
<hgroup>
<h2>More bagging in caret</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">predictors = data.frame(ozone=ozone$ozone)
temperature = ozone$temperature
treebag <- bag(predictors, temperature, B = 10,
bagControl = bagControl(fit = ctreeBag$fit,
predict = ctreeBag$pred,
aggregate = ctreeBag$aggregate))
</code></pre>
<p><a href="http://www.inside-r.org/packages/cran/caret/docs/nbBag">http://www.inside-r.org/packages/cran/caret/docs/nbBag</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-7" style="background:;">
<hgroup>
<h2>Example of custom bagging (continued)</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">plot(ozone$ozone,temperature,col='lightgrey',pch=19)
points(ozone$ozone,predict(treebag$fits[[1]]$fit,predictors),pch=19,col="red")
points(ozone$ozone,predict(treebag,predictors),pch=19,col="blue")
</code></pre>
<div class="rimage center"><img src="fig/unnamed-chunk-2.png" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" class="plot" /></div>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-8" style="background:;">
<hgroup>
<h2>Parts of bagging</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">ctreeBag$fit
</code></pre>
<pre><code>function (x, y, ...)
{
library(party)
data <- as.data.frame(x)
data$y <- y
ctree(y ~ ., data = data)
}
<environment: namespace:caret>
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-9" style="background:;">
<hgroup>
<h2>Parts of bagging</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">ctreeBag$pred
</code></pre>
<pre><code>function (object, x)
{
obsLevels <- levels(object@data@get("response")[, 1])
if (!is.null(obsLevels)) {
rawProbs <- treeresponse(object, x)
probMatrix <- matrix(unlist(rawProbs), ncol = length(obsLevels),
byrow = TRUE)
out <- data.frame(probMatrix)
colnames(out) <- obsLevels
rownames(out) <- NULL
}
else out <- unlist(treeresponse(object, x))
out
}
<environment: namespace:caret>
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-10" style="background:;">
<hgroup>
<h2>Parts of bagging</h2>
</hgroup>
<article data-timings="">
<pre><code class="r">ctreeBag$aggregate
</code></pre>
<pre><code>function (x, type = "class")
{
if (is.matrix(x[[1]]) | is.data.frame(x[[1]])) {
pooled <- x[[1]] & NA
classes <- colnames(pooled)
for (i in 1:ncol(pooled)) {
tmp <- lapply(x, function(y, col) y[, col], col = i)
tmp <- do.call("rbind", tmp)
pooled[, i] <- apply(tmp, 2, median)
}
if (type == "class") {
out <- factor(classes[apply(pooled, 1, which.max)],
levels = classes)
}
else out <- as.data.frame(pooled)
}
else {
x <- matrix(unlist(x), ncol = length(x))
out <- apply(x, 1, median)
}
out
}
<environment: namespace:caret>
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-11" style="background:;">
<hgroup>
<h2>Notes and further resources</h2>
</hgroup>
<article data-timings="">
<p><strong>Notes</strong>:</p>
<ul>
<li>Bagging is most useful for nonlinear models</li>
<li>Often used with trees - an extension is random forests</li>
<li>Several models use bagging in caret's <em>train</em> function</li>
</ul>
<p><strong>Further resources</strong>:</p>
<ul>
<li><a href="http://en.wikipedia.org/wiki/Bootstrap_aggregating">Bagging</a></li>
<li><a href="http://stat.ethz.ch/education/semesters/FS_2008/CompStat/sk-ch8.pdf">Bagging and boosting</a></li>
<li><a href="http://www-stat.stanford.edu/%7Etibs/ElemStatLearn/">Elements of Statistical Learning</a></li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="backdrop"></slide>
</slides>
<div class="pagination pagination-small" id='io2012-ptoc' style="display:none;">
<ul>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=1 title='Bootstrap aggregating (bagging)'>
1
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=2 title='Ozone data'>
2
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=3 title='Bagged loess'>
3
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=4 title='Bagged loess'>
4
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=5 title='Bagging in caret'>
5
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=6 title='More bagging in caret'>
6
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=7 title='Example of custom bagging (continued)'>
7
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=8 title='Parts of bagging'>
8
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=9 title='Parts of bagging'>
9
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=10 title='Parts of bagging'>
10
</a>
</li>
<li>
<a href="#" target="_self" rel='tooltip'
data-slide=11 title='Notes and further resources'>
11
</a>
</li>
</ul>
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