OutOfBag.md (764B)
1 # Out of Bag (OOB) 2 3 ML D5 4 5 **Definition:** Out of bag refers to samples that are not contained within a training sampling for a given predictor when using bagging/pasting. 6 7 It is 37% likely that when using bagging and selecting m random samples from the training set that a given sample will be out of bag. These can be useful because these values can then be used for validation of the individual predictor. 8 9 Here is an example implementation of oob scoring used on a decision tree classifier with scikit learn: 10 11 ```python3 12 13 # Train and then validate predictors on their out of bag samples. 14 15 bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500, oob_score=True, n_jobs=-1, random_state=10) 16 17 bag_clf.fit(X_train, y_train) 18 bag_clf.oob_score_ 19 20 ```