Out Of The Bag Error
Contents |
Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning (classification• regression) Decision
Random Forest Oob Score
trees Ensembles (Bagging, Boosting, Random forest) k-NN Linear regression out of bag prediction Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Out Of Bag Error Cross Validation
Clustering BIRCH Hierarchical k-means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE Structured prediction Graphical models out-of-bag estimation breiman (Bayes net, CRF, HMM) Anomaly detection k-NN Local outlier factor Neural nets Autoencoder Deep learning Multilayer perceptron RNN Restricted Boltzmann machine SOM Convolutional neural network Reinforcement Learning Q-Learning SARSA Temporal Difference (TD) Theory Bias-variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning out of bag typing test VC theory Machine learning venues NIPS ICML JMLR ArXiv:cs.LG Machine learning portal v t e Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating to sub-sample data sampled used for training. OOB is the mean prediction error on each training sample xᵢ, using only the trees that did not have xᵢ in their bootstrap sample.[1] Subsampling allows one to define an out-of-bag estimate of the prediction performance improvement by evaluating predictions on those observations which were not used in the building of the next base learner. Out-of-bag estimates help avoid the need for an independent validation dataset, but often underestimate actual performance improvement and the optimal number of iterations.[2] See also[edit] Boosting (meta-algorithm) Bootstrapping (statistics) Cross-validation (statistics) Random forest Ran
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
Out Of Bag Error In R
Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation breiman [1996b] Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like
Confusion Matrix Random Forest R
you, helping each other. Join them; it only takes a minute: Sign up What is out of bag error in Random Forests? up vote 28 down vote favorite 19 What is out of bag error in Random Forests? https://en.wikipedia.org/wiki/Out-of-bag_error Is it the optimal parameter for finding the right number of trees in a Random Forest? language-agnostic machine-learning classification random-forest share|improve this question edited Jan 24 '14 at 22:21 Max 5,38432753 asked Aug 30 '13 at 21:46 csalive 156123 3 If this question is not implementation specific, you may want to post your question at stats.stackexchange.com –Sentry Sep 2 '13 at 16:27 add a comment| 2 Answers 2 active oldest votes up vote 57 http://stackoverflow.com/questions/18541923/what-is-out-of-bag-error-in-random-forests down vote I will take an attempt to explain: Suppose our training data set is represented by T and suppose data set has M features (or attributes or variables). T = {(X1,y1), (X2,y2), ... (Xn, yn)} and Xi is input vector {xi1, xi2, ... xiM} yi is the label (or output or class). summary of RF: Random Forests algorithm is a classifier based on primarily two methods - Bagging Random subspace method. Suppose we decide to have S number of trees in our forest then we first create S datasets of "same size as original" created from random resampling of data in T with-replacement (n times for each dataset). This will result in {T1, T2, ... TS} datasets. Each of these is called a bootstrap dataset. Due to "with-replacement" every dataset Ti can have duplicate data records and Ti can be missing several data records from original datasets. This is called Bootstrapping. (en.wikipedia.org/wiki/Bootstrapping_(statistics)) Bagging is the process of taking bootstraps & then aggregating the models learned on each bootstrap. Now, RF creates S trees and uses m (=sqrt(M) or =floor(lnM+1)) random subfeatures out of M possible features to create any tree. This is called random subspace method. So for each Ti bootstrap dataset you create a tree Ki. If you want to classify some input data D = {x1, x2, ..., xM} you let
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 http://stats.stackexchange.com/questions/207815/out-of-bag-error-makes-cv-unnecessary-in-random-forests 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 http://www.mathworks.com/help/stats/classificationbaggedensemble.oobloss.html 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 out of rise to the top Out of Bag Error makes CV unnecessary in Random Forests? up vote 3 down vote favorite 2 I am fairly new to random forests. In the past, I have always compared the accuracy of fit vs test against fit vs train to detect any overfitting. But I just read here that: "In random forests, there is no need for cross-validation or a separate test set to get an unbiased out of bag estimate of the test set error. It is estimated internally , during the run..." The small paragraph above can be found under the The out-of-bag (oob) error estimate Section. This Out of Bag Error concept is completely new to me and what's a little confusing is how the OOB error in my model is 35% (or 65% accuracy), but yet, if I apply cross validation to my data (just a simple holdout method) and compare both fit vs test against fit vs train I get a 65% accuracy and a 96% accuracy respectively. In my experience, this is considered overfitting but the OOB holds a 35% error just like my fit vs test error. Am I overfitting? Should I even be using cross validation to check for overfitting in random forests? In short, I am not sure whether I should trust the OOB to get an unbiased error of the test set error when my fit vs train indicates that I am overfitting! cross-validation random-forest overfitting share|improve this question edited Apr 17 at 16:06 asked Apr 17 at 15:58 jgozal 1597 OOB can be used for determining hyper-parameters. Other than that, for me, in order to estimate the performance of a model, one should use cross-validation. –Metariat Apr 17 at 16:03 @Matemattica when you
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 Classification Classification Ensembles Statistics and Machine Learning Toolbox Functions oobLoss On this page Syntax Description Input Arguments Name-Value Pair Arguments Output Arguments Definitions Out of Bag Classification Loss Examples Estimate Out-Of-Bag Error 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 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 oobLossClass: ClassificationBaggedEnsembleOut-of-bag classification errorexpand all in page SyntaxL = oobloss(ens)
L = oobloss(ens,Name,Value)
DescriptionL
= oobloss(ens) returns the classification 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 classification 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