Out-of-bag Classification Error
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Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning (classification• regression) Decision out of bag error random forest trees Ensembles (Bagging, Boosting, Random forest) k-NN Linear regression random forest oob score Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine
Out Of Bag Prediction
(SVM) 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
Out Of Bag Error Cross Validation
models (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 out of bag estimation breiman learning 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) Ra
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Out Of Bag Error In R
or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x breiman [1996b] Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it random forest r 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? Is it the optimal parameter for finding the right https://en.wikipedia.org/wiki/Out-of-bag_error 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 down vote I will take an attempt to explain: Suppose our training data set http://stackoverflow.com/questions/18541923/what-is-out-of-bag-error-in-random-forests 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 it pass through each tree and produce S outputs (one for each tree) which can be denoted by Y = {y1, y2, ..., ys}. Final prediction is
RandomForest(tm) and Random Forest(tm). classification/clustering|regression|survival analysis description|manual|code|papers|graphics|philosophy|copyright|contact us Contents Introduction Overview Features of random forests Remarks How Random Forests work The oob error estimate Variable importance Gini importance Interactions Proximities Scaling Prototypes Missing values https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm for the training set Missing values for the test set Mislabeled cases Outliers Unsupervised learning Balancing prediction error Detecting novelties A case study - microarray data Classification mode Variable importance Using important variables Variable interactions Scaling the data Prototypes Outliers A case study - dna data Missing values in the training set Missing values in the test set Mislabeled cases Case Studies for unsupervised learning Clustering microarray data Clustering dna out of data Clustering glass data Clustering spectral data References Introduction This section gives a brief overview of random forests and some comments about the features of the method. Overview We assume that the user knows about the construction of single classification trees. Random Forests grows many classification trees. To classify a new object from an input vector, put the input vector down each of the trees in the forest. Each tree gives out of bag a classification, and we say the tree "votes" for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Each tree is grown as follows: If the number of cases in the training set is N, sample N cases at random - but with replacement, from the original data. This sample will be the training set for growing the tree. If there are M input variables, a number m<