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<html>
<head>
<style>
body {
font-size: 10pt;
margin: 1.5em;
background-color: lightblue;
color: darkblue;
font-family: Verdana,sans-serif;
}
h1 {
font-size: 1.2em;
font-weight: bold;
margin-top: 2em;
}
h2 {
font-size: 1.1em;
font-weight: bold;
}
fieldset {
width: 740px;
margin-bottom: 12px;
border-color: #00457b;
background-color: #cfeace;
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fieldset div {
margin-bottom: 6px;
font-weight: normal;
}
legend {
border: 2px ridge #00457b;
font-size: 1.2em;
font-weight: bold;
background-color: #e36a51;
color: white;
padding: 8px 16px;
}
</style>
</head>
<body>
<fieldset><legend>./ModelTesting</legend>
<p><a href="https://winvector.github.io/index.html">..</a></p>
<ul>
<pre>
Worked examples for talk: Producing and evaluating machine learning models.
Lecture slides: CV.pdf
Files:
The files below are telegraphic examples used to generate the graphs and numbers in the presentation. Once can in principle work through them using R ( https://cran.r-project.org ), RStudio ( https://www.rstudio.com ), and the referenced packages. They are not complete tutorials, but used to generate the number for the included presentation slides.
For a free video lecture on gradient boosting (one of the methods used) please see here: http://www.win-vector.com/blog/2015/11/free-gradient-boosting-lecture/ .
For a description of the vtreat package (used for data preparation) please see here: http://www.win-vector.com/blog/2016/06/a-demonstration-of-vtreat-data-preparation/ .
CV.pdf : lecture slides.
project.Rproj : RStudio project file (see https://www.rstudio.com ).
installH2O.R : Instructions to install h2o deep learning kit.
kdd2009.Rmd : R knitr/r-markdown neural net fitting/scoring.
kdd2009.html : HTML rendering of above file.
KDD2009vtreat.Rmd : R knitr/r-markdown demonstration fitting/scoring.
KDD2009vtreat.html : HTML rendering of above file.
kdd2009tree.Rmd : R knitr/r-markdown decision tree fitting/scoring.
kdd2009tree.html : HTML rendering of above file.
kdd2009xgboost.Rmd : R knitr/r-markdown demonstration fitting/scoring.
kdd2009xgboost.html : HTML rendering of above file.
orange_small_train.data.gz : Example data.
orange_small_train_churn.labels.txt : Example data.
</pre>
<li><a href="CV.pdf">CV.pdf</a></li>
<li><a href="KDD2009vtreat.Rmd">KDD2009vtreat.Rmd</a></li>
<li><a href="KDD2009vtreat.html">KDD2009vtreat.html</a></li>
<li><a href="README.txt">README.txt</a></li>
<li><a href="installH2O.R">installH2O.R</a></li>
<li><a href="kdd2009.Rmd">kdd2009.Rmd</a></li>
<li><a href="kdd2009.html">kdd2009.html</a></li>
<li><a href="kdd2009tree.Rmd">kdd2009tree.Rmd</a></li>
<li><a href="kdd2009tree.html">kdd2009tree.html</a></li>
<li><a href="kdd2009xgboost.Rmd">kdd2009xgboost.Rmd</a></li>
<li><a href="kdd2009xgboost.html">kdd2009xgboost.html</a></li>
<li><a href="orange_small_train.data.gz">orange_small_train.data.gz</a></li>
<li><a href="orange_small_train_churn.labels.txt">orange_small_train_churn.labels.txt</a></li>
<li><a href="project.Rproj">project.Rproj</a></li>
</ul>
</fieldset></body>
</html>