From e2ff3f5877e687f3327fc18180739630c4c35f81 Mon Sep 17 00:00:00 2001 From: amete Date: Wed, 5 Apr 2017 15:31:25 +0200 Subject: [PATCH] minor addition --- project_writeup.html | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/project_writeup.html b/project_writeup.html index a972b56..c27599a 100644 --- a/project_writeup.html +++ b/project_writeup.html @@ -348,6 +348,22 @@

Analysis

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On the expected out of sample error

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Please note that since the method random forest 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: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#ooberr +

+ Copyright Ⓒ Alaettin Serhan Mete, 2017. All rights reserved.