oobPredict - Class: ClassificationBaggedEnsemble

Predict out-of-bag response of ensemble

Syntax

Description

[label,score] = oobPredict(ens) returns class labels and scores for ens for out-of-bag data.

[label,score] = oobPredict(ens,Name,Value) computes labels and scores with additional options specified by one or more Name,Value pair arguments.

Input Arguments

Name-Value Pair Arguments

Output Arguments

Definitions

Out of Bag

Bagging, which stands for "bootstrap aggregation", is a type of ensemble learning. To bag a weak learner such as a decision tree on a dataset, fitensemble generates many bootstrap replicas of the dataset and grows decision trees on these replicas. fitensemble obtains each bootstrap replica by randomly selecting N observations out of N with replacement, where N is the dataset size. To find the predicted response of a trained ensemble, predict take an average over predictions from individual trees.

Drawing N out of N observations with replacement omits on average 37% (1/e) of observations for each decision tree. These are "out-of-bag" observations. For each observation, oobLoss estimates the out-of-bag prediction by averaging over predictions from all trees in the ensemble for which this observation is out of bag. It then compares the computed prediction against the true response for this observation. It calculates the out-of-bag error by comparing the out-of-bag predicted responses against the true responses for all observations used for training. This out-of-bag average is an unbiased estimator of the true ensemble error.

Score (ensemble)

For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.

Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:

• AdaBoostM1 scores range from –∞ to ∞.

• Bag scores range from 0 to 1.

Examples

Find the out-of-bag predictions and scores for the Fisher iris data. Find the scores in the range (0.2,0.8); these are the scores where there is notable uncertainty in the resulting classifications.

load fisheriris
ens = fitensemble(meas,species,'Bag',100,...
    'Tree','type','classification');
[label score] = oobPredict(ens);
unsure = ( (score > .2) & (score < .8));
sum(sum(unsure)) % How many uncertain predictions?

ans =
    16

See Also

oobEdge | oobLoss | oobMargin | oobPredict | predict

How To

• Nonparametric Supervised Learning