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amete merged 1 commit into from
Apr 5, 2017
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minor addition #3

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16 changes: 16 additions & 0 deletions project_writeup.html
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Expand Up @@ -348,6 +348,22 @@ <h1> Analysis </h1>

</section>

<h3> On the expected out of sample error </h3>
<p> Please note that since the method <i>random forest</i> is chosen, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. This is explained as:
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"In random forests, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. It is estimated internally, during the run, as follows:
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Each tree is constructed using a different bootstrap sample from the original data. About one-third of the cases are left out of the bootstrap sample and not used in the construction of the kth tree.
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Put each case left out in the construction of the kth tree down the kth tree to get a classification. In this way, a test set classification is obtained for each case in about one-third of the trees. At the end of the run, take j to be the class that got most of the votes every time case n was oob. The proportion of times that j is not equal to the true class of n averaged over all cases is the oob error estimate. This has proven to be unbiased in many tests."
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The reader can find more information at: <a href="https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#ooberr">https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#ooberr</a>
</p>

<b>Copyright &#9400; Alaettin Serhan Mete, 2017. All rights reserved.</b>

</body>
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