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resubPredict

Predict ensemble response by resubstitution

Sintaxis

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.

Argumentos de entrada

ens

A classification ensemble created with fitcensemble.

Argumentos de par nombre-valor

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.

Predeterminado: 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.

Ejemplos

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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

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