Out Of Bag 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 trees Ensembles (Bagging, Boosting, Random forest) k-NN Linear regression Naive Bayes Neural networks random forest oob score Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM) Clustering out of bag prediction BIRCH Hierarchical k-means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE
Out Of Bag Error Cross Validation
Structured prediction Graphical 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
Out-of-bag Estimation Breiman
Learning Q-Learning SARSA Temporal Difference (TD) Theory Bias-variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical 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 out of bag typing test 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 Random subspace method (attribute bagging) References[edit] ^ Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. pp.316–321. ^ Ridgeway, Greg (2007). Generalized Boosted Models: A guide to the gbm package. This computer science article is a stub. You can help Wikipedia by expanding it. v t e Retrieved from "https://en.wikipedia.org/w/index.php?title=Out-of-bag_error&oldid=730570484" Categories: Ensemble learningMachine learning algorithmsComputational statisticsComputer science stubsHidden categories: All stub articles Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespaces Artic
Random Forests?What does it mean? What's a typical value, if any? Why would it be higher or lower than a typical value?UpdateCancelAnswer Wiki5 Answers Manoj Awasthi, Machine learning newbie.Written 158w agoI will take an
Breiman [1996b]
attempt to explain: Suppose our training data set is represented by T and suppose out of bag error in r data set has M features (or attributes or variables).T = {(X1,y1), (X2,y2), ... (Xn, yn)} and Xi is input vector {xi1, confusion matrix random forest r xi2, ... xiM} and yi is the label (or output or class). summary of RF: Random Forests algorithm is a classifier based on primarily two methods - bagging and random subspace method. Suppose we decide https://en.wikipedia.org/wiki/Out-of-bag_error 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 https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests original datasets. This is called Bagging. 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 a majority vote on this set. Out-of-bag error:After creating the classifiers (S trees), for each (Xi,yi) in the original training set i.e. T, select all Tk which does not include (Xi,yi). This subset, pay attention, is a set of boostrap datasets which does not contain a particular record from the original dataset. This set is called out-of-bag examples. There are n such subsets (one for each data record in original dataset T). OOB classifier is the aggregation of votes ONLY over Tk such that it does not contain (xi,yi). Out-of-bag estimate for the generalization error is the error rate of the out-of-bag classifier on the training set (compare it with known yi's).Why is it important?The study of error estimates for bag
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 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