Out Of Bag Error In Random Forests
Contents |
Random Forests?What does it mean? What's a typical value, if any? Why would it be higher or lower than a typical value?UpdateCancelPromoted by NVIDIAGTC DC deep learning conference.Must see labs, demos and training random forest oob score with AI experts. Free for government employees.Learn More at Dc.gputechconf.comAnswer Wiki5 Answers Manoj Awasthi, out of bag prediction Machine learning newbie.Written 158w agoI will take an attempt to explain: Suppose our training data set is represented by T and out of bag error cross validation 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} and yi is the label (or output or class). out of bag estimation breiman summary 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 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
Out Of Bag Typing Test
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 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 ge
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 Overflow confusion matrix random forest r the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags outofbag typing 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; it only https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests 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 22:21 Max http://stackoverflow.com/questions/18541923/what-is-out-of-bag-error-in-random-forests 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" every dataset Ti can have duplicate
Random Forests?What does it mean? What's a typical value, if any? Why would it be higher or lower than a typical value?UpdateCancelPromoted by NVIDIAGTC DC deep learning conference.Must see labs, demos and training with AI experts. Free for government employees.Learn https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests More at Dc.gputechconf.comAnswer Wiki5 Answers Manoj Awasthi, Machine learning newbie.Written 158w agoI will take an https://www.kaggle.com/c/titanic/forums/t/3554/implications-of-out-of-bag-oob-error-in-random-forest-models 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} 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 - 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 can have duplicate data records and Ti can be missing out of bag 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 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 estimat
28 Sep 2012 Sat 31 Dec 2016 (2 months to go) Dashboard ▼ Home Data Make a submission Information Description Evaluation Rules Prizes Frequently Asked Questions Getting Started With Excel Getting Started With Python Getting Started With Python II Getting Started With Random Forests New: Getting Started with R Submission Instructions Forum Kernels New Script New Notebook Leaderboard Visualization Competition Forum All Forums » Titanic: Machine Learning from Disaster Implications of Out-of-bag (OOB) error in Random Forest models Start Watching « Prev Topic » Next Topic 0 votes I've been trying out a lot of models and a lot of different ways of manipulating and selecting features for this competition, always to be dissappointed by the relatively very small amount of changes in the score, even when I submit two very fundamentally different sets of predictions. I know the test set for the public leaderboard is only a random half of the actual test set so maybe that's the reason but it still feels weird. My question is also related to thisphenomenon: I'm training a random forest model on most of the features, some being modified and one more extra feature added. When I check the model, I can see the OOB error value which for my latest iterations is around 16%. This suggests that my model has 84% out of sample accuracy for the training set. Note that the model calculates the error using observations not trained on for each decision tree in the forest and aggregates over all so there should be no bias, hence the name out-of-bag. Every source on random forest methods I've read states that this should be an accurate estimate of the test error. However when I submit the results they hover around in the 76%-78% range with generally very small changes. One explanation I can make for this is what I pointed out in the first paragraph, maybe I'm just unlucky and the random