Out Of Bag Error Matlab
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Out Of Bag Estimate
Machine Learning Toolbox Examples Functions and Other Reference Release Notes PDF Documentation treebagger oobError On this page Syntax Description Algorithms See Also This is machine translation Translated by Mouse over random forests text to see original. Click the button below to return to the English verison of the page. Back to English × Translate This Page Select Language Bulgarian Catalan Chinese Simplified Chinese Traditional Czech Danish Dutch English Estonian Finnish French German Greek Haitian Creole Hindi Hmong Daw Hungarian Indonesian Italian Japanese Korean Latvian Lithuanian Malay Maltese Norwegian Polish Portuguese Romanian Russian Slovak Slovenian Spanish Swedish Thai Turkish Ukrainian Vietnamese Welsh MathWorks Machine Translation The automated translation of this page is provided by a general purpose third party translator tool. MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. Translate oobErrorClass: TreeBaggerOut-of-bag error Syntaxerr = oobError(B)
err = oobError(B,'param1',val1,'param2',val2,...)
Descriptionerr = oobError(B) computes the misclassification probability (for classification trees) or mean squared error (for regression trees) for out-of-bag observations in the training data, using the trained bagger B. err is a vector of length NTrees, where NTrees is the number of trees in the ensemble. err = oobError(B,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:'Mode'Character vector indicating how oobError computes errors. If set to 'cumulative' (default), the method computes cumulative errors and err is a vector of length NTrees, where the first element gives error from trees(1), second element gives error from trees(1:2) etc., up to trees(1:NTrees). If set to 'individual', err is a vector of length NTrees, where each element is an error from each tree in the ensemble. If set to 'ensemble', err is a scalar showing the cumulative error for the entire ensemble. 'Trees'Vector of indices indicating w
Search All Support Resources Support Documentation MathWorks Search MathWorks.com MathWorks Documentation Support Documentation Toggle navigation Trial Software Product Updates Documentation Home Statistics and Machine Learning Toolbox Examples Functions and Other Reference Release Notes PDF Documentation oobLoss On this page Syntax Description Input Arguments Name-Value Pair Arguments Output Arguments Definitions Out of Bag Examples See Also This is machine translation Translated by Mouse over text to see original. Click the button below to return to the English verison of the page. Back to English × Translate This Page Select Language Bulgarian Catalan http://www.mathworks.nl/help/stats/treebagger.ooberror.html Chinese Simplified Chinese Traditional Czech Danish Dutch English Estonian Finnish French German Greek Haitian Creole Hindi Hmong Daw Hungarian Indonesian Italian Japanese Korean Latvian Lithuanian Malay Maltese Norwegian Polish Portuguese Romanian Russian Slovak Slovenian Spanish Swedish Thai Turkish Ukrainian Vietnamese Welsh MathWorks Machine Translation The automated translation of this page is provided by a general purpose third party translator tool. MathWorks does not http://www.mathworks.nl/help/stats/regressionbaggedensemble.oobloss.html warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. Translate oobLossClass: RegressionBaggedEnsembleOut-of-bag regression error SyntaxL = oobLoss(ens)
L = oobLoss(ens,Name,Value)
DescriptionL
= oobLoss(ens) returns the mean squared error for ens computed for out-of-bag data.L
= oobLoss(ens,Name,Value) computes error with additional options specified by one or more Name,Value pair arguments. You can specify several name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN.Input Argumentsens A regression bagged ensemble, constructed with fitensemble. Name-Value Pair ArgumentsSpecify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.'learners' Indices of weak learners in the ensemble ranging from 1 to NumTrained. oobLoss uses only these learners for calculating loss. Default: 1:NumTrained'lossfun' Function handle for loss function, or 'mse', meaning mean squared error. If you pass a function handle fun, oobLoss calls it as FUN(Y,Yfit,W) where Y, Yfit, and W are numeric vectors of the same length. Y is the observed response, Yfit is the predicte
Support Answers MathWorks Search MathWorks.com MathWorks Answers Support MATLAB Answers™ MATLAB Central Community Home MATLAB Answers File Exchange Cody Blogs Newsreader Link https://www.mathworks.com/matlabcentral/answers/129549-questions-about-oob-error-in-treebagger Exchange ThingSpeak Anniversary Home Ask Answer Browse More Contributors Recent Activity Flagged Content Flagged as Spam Help MATLAB Central Community Home MATLAB Answers File Exchange Cody Blogs Newsreader http://stats.stackexchange.com/questions/34625/interpreting-tree-classification-errors-in-matlab Link Exchange ThingSpeak Anniversary Home Ask Answer Browse More Contributors Recent Activity Flagged Content Flagged as Spam Help Trial software Emmanuel (view profile) 1 question 1 answer 0 out of accepted answers Reputation: 0 Vote0 Questions about OOB error in TreeBagger Asked by Emmanuel Emmanuel (view profile) 1 question 1 answer 0 accepted answers Reputation: 0 on 14 May 2014 Latest activity Answered by Emmanuel Emmanuel (view profile) 1 question 1 answer 0 accepted answers Reputation: 0 on 27 May 2014 18 views (last 30 days) 18 out of bag views (last 30 days) Hello,I'm currently working on a classification problem with random forests and am using Matlab's TreeBagger. To estimate the discriminant power of my features, I would like to visualize the prediction ratio for each class. So far I used a train and test set, and given that each forest gives a slightly different result due to its random nature, I build 100 forests and average the ratios.However, on Breiman's site (
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Interpreting tree classification errors in Matlab up vote 0 down vote favorite I am currently using Matlab to generate random forests. I am using the TreeBagger class with the function oobError. I can plot a 2D figure which puts my tree count on the X-axis (IKA weak learners count) and classification error is on the Y-axis. My question is: How can I interpret the actual error of my classifier (something like cross-validation which gives you a double as your classification error)? Another question is: Is the X-axis of my figure the number of trees in bag or is it describing which tree (tree #50 for example) has the accuracy given at the corresponding point on the Y-axis? cross-validation matlab cart out-of-sample share|improve this question edited Aug 19 '12 at 13:09 MansT 7,0202851 asked Aug 19 '12 at 12:09 Green Code 565 what do you mean by "gives you a double"? Are you referring to estimates of the two types of classification errors that can occur in a two-class problem? –Michael Chernick Aug 19 '12 at 13:34 add a comment| 1 Answer 1 active oldest votes up vote 2 down vote If you're doing something like the example in the matlab documentation, then the plot contains the Out-Of-Bag Error as a function of the total number of trees. If you want to cross-validate your model (which you should!), then build the TreeBagger object with some portion of your data and apply it the remaining held-out data with the predict() method. Matlab has a bunch of utility functions to make cross-validation easier. Take a look at the cvpartition class and the crossval function, though it's obviously not too difficult to write your own versions either. share|improve this answer answered Aug 19 '12 at 13:38 Matt Krause 10.5k12158 1 Thanks for your answer, however, I have read many articles and other stuff that the out of bag error is the real measure of the CCR in random forest. The k-fold cross validation method may not be suitable. –Green Code Aug 26 '12 at 10:13 Interesting! Lookin