Oob Error Rate
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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 random forest oob score agoI will take an attempt to explain: Suppose our training data set is out-of-bag error in r represented by T and suppose data set has M features (or attributes or variables).T = {(X1,y1), (X2,y2), ... (Xn, yn)} out of bag prediction and Xi is input vector {xi1, 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 out of bag error cross validation - bagging and 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
Out Of Bag Estimation Breiman
can have duplicate data records and Ti can be missing several data records from 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
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Confusion Matrix Random Forest R
company Business Learn more about hiring developers or posting ads with us Cross Validated Questions out of bag typing test Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, random forest algorithm 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 https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests the top How to interpret OOB and confusion matrix for random forest? up vote 28 down vote favorite 20 I got a an R script from someone to run a random forest model. I modified and run it with some employee data. We are trying to predict voluntary separations. Here is some additional info: this is a classification model were 0 = employee stayed, 1= employee terminated, we are currently only looking at a dozen http://stats.stackexchange.com/questions/30691/how-to-interpret-oob-and-confusion-matrix-for-random-forest predictor variables, the data is "unbalanced" in that the term'd records make up about 7% of the total record set. I run the model with various mtry and ntree selections but settled on the below. The OOB is 6.8% which I think is good but the confusion matrix seems to tell a different story for predicting terms since the error rate is quite high at 92.79% Am I right in assuming that I can't rely on and use this model because the high error rate for predicting terms? or is there something also I can do to use RF and get a smaller error rate for predicting terms? FOREST_model <- randomForest(theFormula, data=trainset, mtry=3, ntree=500, importance=TRUE, do.trace=100) ntree OOB 1 2 100: 6.97% 0.47% 92.79% 200: 6.87% 0.36% 92.79% 300: 6.82% 0.33% 92.55% 400: 6.80% 0.29% 92.79% 500: 6.80% 0.29% 92.79% > print(FOREST_model) Call: randomForest(formula = theFormula, data = trainset, mtry = 3, ntree = 500, importance = TRUE, do.trace = 100) Type of random forest: classification Number of trees: 500 No. of variables tried at each split: 3 OOB estimate of error rate: 6.8% Confusion matrix: 0 1 class.error 0 5476 16 0.002913328 1 386 30 0.927884615 > nrow(trainset) [1] 5908 r classification error random-forest share|improve this question edited Jun 19 '12 at 20:37 chl♦ 37.6k6125243 asked Jun 18 '12 a
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 https://en.wikipedia.org/wiki/Out-of-bag_error Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (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 models (Bayes net, CRF, HMM) Anomaly detection k-NN Local outlier factor Neural nets Autoencoder Deep learning Multilayer perceptron RNN Restricted out of 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 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 out of bag 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 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 learn
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