Define Out Of Bag Error
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Out Of Bag Error Estimate
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Out Of Bag Error Wiki
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Out Of Bag Error Weka
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 error of random forests, boosted decision trees, and out of bag error in r 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 learningMachine learning algorithmsComputational statisticsComputer science stubsHidden categories: All stub articles Navigation menu Personal tools Not logged inTalkContr
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 156w agoI will take an attempt to explain: Suppose our training data set is out of bag error in random forest represented by T and suppose data set has M features (or attributes or variables).T = out of bag error rate random forest {(X1,y1), (X2,y2), ... (Xn, yn)} and Xi is input vector {xi1, xi2, ... xiM} and yi is the label (or output or class). summary cat out of the bag meaning of RF: Random Forests algorithm is a classifier based on primarily two methods - 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 https://en.wikipedia.org/wiki/Out-of-bag_error 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 Bagging. Now, RF creates S trees and uses m (=sqrt(M) or =floor(lnM+1)) random subfeatures out of M possible features to https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests 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 bagged classifiers in Breiman [1996b], gives empirical evidence to show that the out-of-bag estimate is as accurate as using a test set of the same size as the training set. Therefore, using the out-of-bag error estimate removes the need for a set aside test set.Typical value etc.? It gives you so
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/70704/interpreting-out-of-bag-error-estimate-for-randomforestregressor 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 out of and rise to the top Interpreting out of bag error estimate for RandomForestRegressor up vote 8 down vote favorite 1 I am using RandomForest regressor on my data and I could see that the oob score was obtained to be 0.83. I am not sure how it came out to be like this. I mean my targets are high values in the range of 10^7. So if it's MSE then it should have out of bag been much higher. I don't understand what 0.83 signify here. I am using python's RandomForestRegressor of the sklearn toolkit. I do model = RandomForestRegressor(max_depth=7, n_estimators=100, oob_score=True, n_jobs=-1) model.fit(trainX, trainY ) Then I see model.oob_score_ and I get values like 0.83809026152005295 regression random-forest share|improve this question edited Sep 23 '13 at 16:14 asked Sep 22 '13 at 16:57 user34790 1,45331442 @Momo. I am using python's sklearn.ensemble's RandomForestRegressor. I just use the model like –user34790 Sep 23 '13 at 16:13 add a comment| 1 Answer 1 active oldest votes up vote 5 down vote In order to compare the ground truth (i.e. correct/actual) target values with estimated (i.e. predicted) target values by the random forest , scikit-learn doesn't use the MSE but $R^2$ (unlike e.g. MATLAB or (Breiman 1996b)), as you can see in the code of forest.py: self.oob_score_ = 0.0 for k in xrange(self.n_outputs_): self.oob_score_ += r2_score(y[:, k], predictions[:, k]) self.oob_score_ /= self.n_outputs_ r2_score() computes the coefficient of determination aka. R2, whose best possible score is 1.0, and lower values are worse. FYI: What is the out of bag error in Random Forests? What is the difference between “coefficient of determination” and “mean squared error”? Breiman, Leo. Out-of-bag estimation. Technical report, Statistics Department, University of California Berkeley, Berkeley CA 94708, 1996b. 33, 34, 1996. share|