Out Of Bag Error Rate
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?UpdateCancelAnswer Wiki5 Answers Manoj Awasthi, Machine learning newbie.Written 158w agoI will take an attempt to explain: random forest oob score Suppose our training data set is represented by T and suppose data set has
Out Of Bag Prediction
M features (or attributes or variables).T = {(X1,y1), (X2,y2), ... (Xn, yn)} and Xi is input vector {xi1, xi2, ... xiM} and
Out Of Bag Error Cross Validation
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 decide to have S number of trees
Out-of-bag Estimation Breiman
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 data records from original datasets. This is called Bagging. Now, RF out of bag typing test 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 estimate is as accurate
RandomForest(tm) and Random Forest(tm). classification/clustering|regression|survival analysis description|manual|code|papers|graphics|philosophy|copyright|contact us Contents Introduction Overview Features of random forests Remarks How Random Forests work The oob error breiman [1996b] estimate Variable importance Gini importance Interactions Proximities Scaling Prototypes Missing values for out of bag error in r the training set Missing values for the test set Mislabeled cases Outliers Unsupervised learning Balancing prediction error Detecting random forest r novelties A case study - microarray data Classification mode Variable importance Using important variables Variable interactions Scaling the data Prototypes Outliers A case study - dna data Missing values in https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests the training set Missing values in the test set Mislabeled cases Case Studies for unsupervised learning Clustering microarray data Clustering dna data Clustering glass data Clustering spectral data References Introduction This section gives a brief overview of random forests and some comments about the features of the method. Overview We assume that the user knows about the construction of single classification https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm trees. Random Forests grows many classification trees. To classify a new object from an input vector, put the input vector down each of the trees in the forest. Each tree gives a classification, and we say the tree "votes" for that class. The forest chooses the classification having the most votes (over all the trees in the forest). Each tree is grown as follows: If the number of cases in the training set is N, sample N cases at random - but with replacement, from the original data. This sample will be the training set for growing the tree. If there are M input variables, a number m< 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 https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests Manoj Awasthi, Machine learning newbie.Written 158w agoI 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} and yi is the label (or output or class). summary of RF: out of 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, ... out of bag 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 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