Out Of Bag Error Estimate
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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 random forest oob score Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector
Out-of-bag Estimation Breiman
machine (SVM) Clustering BIRCH Hierarchical k-means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift Dimensionality reduction Factor analysis CCA ICA out of bag prediction 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 Boltzmann out of bag error cross validation 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 error of
Out Of Bag Typing Test
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 learningMachine learning algorithmsComputational stat
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings breiman [1996b] and policies of this site About Us Learn more about Stack out of bag error in r Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs
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Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; https://en.wikipedia.org/wiki/Out-of-bag_error 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? 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 http://stackoverflow.com/questions/18541923/what-is-out-of-bag-error-in-random-forests 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 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" ever
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 http://stats.stackexchange.com/questions/70704/interpreting-out-of-bag-error-estimate-for-randomforestregressor policies of this site About Us Learn more about Stack Overflow 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 out of 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 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 out of bag 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 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 1,45331543 @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_ /= se
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