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<!DOCTYPE html>
<html>
<head>
<title>Practical Machine Learning Overview</title>
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<meta name="description" content="Practical Machine Learning Overview">
<meta name="author" content="Jeffrey Leek">
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<hgroup class="auto-fadein">
<h1>Practical Machine Learning Overview</h1>
<h2></h2>
<p>Jeffrey Leek<br/>Johns Hopkins Bloomberg School of Public Health</p>
</hgroup>
</slide>
<!-- SLIDES -->
<slide class="" id="slide-1" style="background:;">
<hgroup>
<h2>Practical Machine Learning Content</h2>
</hgroup>
<article>
<ul>
<li>Prediction study design</li>
<li>Types of Errors</li>
<li>Cross validation</li>
<li>The caret package</li>
<li>Plotting for prediction</li>
<li>Preprocessing</li>
<li>Predicting with regression</li>
<li>Predicting with trees</li>
<li>Boosting</li>
<li>Bagging</li>
<li>Model blending </li>
<li>Forecasting </li>
</ul>
</article>
<!-- Presenter Notes -->
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<slide class="" id="slide-2" style="background:;">
<hgroup>
<h2>Basic terms</h2>
</hgroup>
<article>
<p>In general, <strong>Positive</strong> = identified and <strong>negative</strong> = rejected. Therefore:</p>
<ul>
<li><strong>True positive</strong> = correctly identified</li>
<li><strong>False positive</strong> = incorrectly identified</li>
<li><strong>True negative</strong> = correctly rejected</li>
<li><strong>False negative</strong> = incorrectly rejected</li>
</ul>
<p><em>Medical testing example</em>:</p>
<ul>
<li><strong>True positive</strong> = Sick people correctly diagnosed as sick</li>
<li><strong>False positive</strong>= Healthy people incorrectly identified as sick</li>
<li><strong>True negative</strong> = Healthy people correctly identified as healthy</li>
<li><strong>False negative</strong> = Sick people incorrectly identified as healthy.</li>
</ul>
<p><a href="http://en.wikipedia.org/wiki/Sensitivity_and_specificity">http://en.wikipedia.org/wiki/Sensitivity_and_specificity</a></p>
</article>
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<hgroup>
<h2>Correlated predictors</h2>
</hgroup>
<article>
<pre><code class="r">library(caret)
library(kernlab)
data(spam)
inTrain <- createDataPartition(y = spam$type, p = 0.75, list = FALSE)
training <- spam[inTrain, ]
testing <- spam[-inTrain, ]
M <- abs(cor(training[, -58]))
diag(M) <- 0
which(M > 0.8, arr.ind = T)
</code></pre>
<pre><code>## row col
## num415 34 32
## direct 40 32
## num857 32 34
## num857 32 40
</code></pre>
</article>
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<hgroup>
<h2>Basic idea behind boosting</h2>
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<article>
<ol>
<li>Start with a set of classifiers \(h_1,\ldots,h_k\)
<ul>
<li>Examples: All possible trees, all possible regression models, all possible cutoffs.</li>
</ul></li>
<li>Create a classifier that combines classification functions:
\(f(x) = \rm{sgn}\left(\sum_{t=1}^T \alpha_t h_t(x)\right)\).
<ul>
<li>Goal is to minimize error (on training set)</li>
<li>Iterative, select one \(h\) at each step</li>
<li>Calculate weights based on errors</li>
<li>Upweight missed classifications and select next \(h\)</li>
</ul></li>
</ol>
<p><a href="http://en.wikipedia.org/wiki/AdaBoost">Adaboost on Wikipedia</a></p>
<p><a href="http://webee.technion.ac.il/people/rmeir/BoostingTutorial.pdf">http://webee.technion.ac.il/people/rmeir/BoostingTutorial.pdf</a></p>
</article>
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