Out Of Bag Error Weka
Contents |
Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning (classification• regression) Decision oob error random forest r 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 error cross validation (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 In R
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 how to calculate out of bag error 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
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
Out Of Bag Estimation Breiman
an attempt to explain: Suppose our training data set is represented by T and
Breiman [1996b]
suppose data set has M features (or attributes or variables).T = {(X1,y1), (X2,y2), ... (Xn, yn)} and Xi is input vector out of bag score {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 - bagging and random subspace method. Suppose we https://en.wikipedia.org/wiki/Out-of-bag_error 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 https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests 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 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 stud
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| Report Content as Inappropriate ♦ ♦ Bagging: out-of-bag error implementation question Its quite likely that I am mistaken in my understanding, but it seems to me that the out-of-bag error value returned by the Bagging implementation (weka/classifiers/meta/Bagging.java $Revision: 1.38 $) might be the in-bag error estimate instead. The code on lines 547-557 is as follows: for (int j = 0; j < m_Classifiers.length; j++) { if (!inBag[j][i]) continue; voteCount++; double pred = m_Classifiers[j].classifyInstance(data.instance(i)); if (numeric) votes[0] += pred; else votes[(int) pred]++; } As I understand, the inBag[][] array contains boolean flags indicating what training data points were sampled for each classifier in the ensemble. The code above (line "if (!inBag[j][i])") would therefore skip the data points that are out-of-bag for a classifier, and evaluate only the in-bag ones. Where-as for the out-of-bag error estimate, we would want to evaluate exactly the complimentary set. Could someone please help me resolve if and where I might be going wrong. Thanks, Mahesh ____________________________________________________________________________________Choose the right car based on your needs. Check out Yahoo! Autos new Car Finder tool. http://autos.yahoo.com/carfinder/_______________________________________________ Wekalist mailing list [hidden email] https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist Peter Reutemann Reply | Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: Bagging: out-of-bag error implementation question > Its quite likely that I am mistaken in my understanding, but it seems > to me that the out-of-bag error value returned by the Bagging > implementation (weka/classifiers/meta/Bagging.java $Revision: 1.38 $) > might be the in-bag error estimate instead. ... > As I understand, the inBag[][] array contains boolean flags > indicating what training data points were sampled for each classifier > in the ensemble. The code above (line "if (!inBag[j][i])") would > therefore skip the data points that are out-of-bag for a classifier, > and evaluate only the in-bag ones. Where-as for the out-of-bag error > estimate, we would want to evaluate exactly the complimentary set. > Could someone please help me resolve if and where I might be going > wrong. You're not wrong, it is a bug. The previous implementation was generating a wrong error as well. Anyway, just committed