resubPredict

Classify observations in ensemble of classification models

Syntax

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

Description

label = resubPredict(ens) returns the labels ens predicts for the data ens.X. label is the predictions of ens on the data that fitcensemble used to create ens.

[label,score] = resubPredict(ens) also returns scores for all classes.

[label,score] = resubPredict(ens,Name,Value) finds resubstitution predictions with additional options specified by one or more Name,Value pair arguments.

Input Arguments

ens

A classification ensemble created with fitcensemble.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'learners'

Indices of weak learners in the ensemble ranging from 1 to NumTrained. oobLoss uses only these learners for calculating loss.

Default: 1:NumTrained

Output Arguments

`label`	The response `ens` predicts for the training data. `label` is the same data type as the training response data `ens.Y`, and has the same number of entries as the number of rows in `ens.X`.
`score`	An `N`-by-`K` matrix, where `N` is the number of rows in `ens.X`, and `K` is the number of classes in `ens`. High score value indicates that an observation likely comes from this class.

Examples

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Find Number of Ensemble Misclassifications

Open Live Script

Find the total number of misclassifications of the fisheriris data for a classification ensemble.

Load the Fisher iris data set.

load fisheriris

Train an ensemble of 100 boosted classification trees using AdaBoostM2.

t = templateTree('MaxNumSplits',1); % Weak learner template tree object
ens = fitcensemble(meas,species,'Method','AdaBoostM2','Learners',t);

Find the total number of misclassifications.

Ypredict = resubPredict(ens); % The predictions
Ysame = strcmp(Ypredict,species); % True when Ypredict and species are equal
sum(~Ysame) % Number of different predictions

ans = 5

More About

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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.

Documentation