oobPredict

Predict out-of-bag response of ensemble

Syntax

[label,score] = oobPredict(ens)
[label,score] = oobPredict(ens,Name,Value)


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

ens	A classification bagged ensemble, constructed with fitcensemble.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Learners — Indices of weak learners

[1:ens.NumTrained] (default) | vector of positive integers

Indices of weak learners in the ensemble to use in oobPredict, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, all learners are used.

Example: Learners=[1 2 4]

Data Types: single | double

UseParallel —

Indication to perform inference in parallel, specified as false (compute serially) or true (compute in parallel). Parallel computation requires Parallel Computing Toolbox™. Parallel inference can be faster than serial inference, especially for large datasets. Parallel computation is supported only for tree learners.

Output Arguments

label	Classification labels of the same data type as the training data Y. (The software treats string arrays as cell arrays of character vectors.) There are N elements or rows, where N is the number of training observations. The label is the class with the highest score. In case of a tie, the label is earliest in ens.ClassNames.
score	An N-by-K numeric matrix for N observations and K classes. A high score indicates that an observation is likely to come from this class. Scores are in the range 0 to 1.

Examples

Find Out-of-Bag Response of Classification Ensemble

Find the out-of-bag predictions and scores for the Fisher iris data. Find the scores with notable uncertainty in the resulting classifications.

Load the sample data set.

load fisheriris

Train an ensemble of bagged classification trees.

ens = fitcensemble(meas,species,'Method','Bag');

Find the out-of-bag predictions and scores.

[label,score] = oobPredict(ens);

Find the scores in the range (0.2,0.8). These scores have notable uncertainty in the resulting classifications.

unsure = ((score > .2) & (score < .8));
sum(sum(unsure))  % Number of uncertain predictions

ans = 16

More About

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, fitcensemble generates many bootstrap replicas of the dataset and grows decision trees on these replicas. fitcensemble 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.

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the UseParallel name-value argument to true in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).