PUMA publications for /author/Leo%20Breiman/ensemble%20learninghttps://puma.uni-kassel.de/author/Leo%20Breiman/ensemble%20learningPUMA RSS feed for /author/Leo%20Breiman/ensemble%20learning2024-03-29T02:17:33+01:00Random Forestshttps://puma.uni-kassel.de/bibtex/2b8187107bf870043f2f93669958858f1/stephandoerfelstephandoerfel2015-03-19T20:35:07+01:00ensemble classification forest learning random <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leo Breiman" itemprop="url" href="/author/Leo%20Breiman"><span itemprop="name">L. Breiman</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Machine Learning</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">45 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">5-32</span></em> </span>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Thu Mar 19 20:35:07 CET 2015Machine Learning15-32Random Forests452001ensemble classification forest learning random Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Random Forests - Springer