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Classification error


L = loss(ens,tbl,ResponseVarName)
L = loss(ens,tbl,Y)
L = loss(ens,X,Y)
L = loss(___,Name,Value)


L = loss(ens,tbl,ResponseVarName) returns the classification error for ensemble ens computed using table of predictors tbl and true class labels tbl.ResponseVarName.

L = loss(ens,tbl,Y) returns the classification error for ensemble ens computed using table of predictors tbl and true class labels Y.

L = loss(ens,X,Y) returns the classification error for ensemble ens computed using matrix of predictors X and true class labels Y.

L = loss(___,Name,Value) computes classification error with additional options specified by one or more Name,Value pair arguments, using any of the previous syntaxes.

When computing the loss, loss normalizes the class probabilities in ResponseVarName or Y to the class probabilities used for training, stored in the Prior property of ens.

Input Arguments


Classification ensemble created with fitcensemble, or a compact classification ensemble created with compact.


Sample data, specified as a table. Each row of tbl corresponds to one observation, and each column corresponds to one predictor variable. tbl must contain all of the predictors used to train the model. Multi-column variables and cell arrays other than cell arrays of character vectors are not allowed.

If you trained ens using sample data contained in a table, then the input data for this method must also be in a table.


Response variable name, specified as the name of a variable in tbl.

You must specify ResponseVarName as a character vector or string scalar. For example, if the response variable Y is stored as tbl.Y, then specify it as 'Y'. Otherwise, the software treats all columns of tbl, including Y, as predictors when training the model.


Matrix of data to classify. Each row of X represents one observation, and each column represents one predictor. X must have the same number of columns as the data used to train ens. X should have the same number of rows as the number of elements in Y.

If you trained ens using sample data contained in a matrix, then the input data for this method must also be in a matrix.


Class labels of observations in tbl or X. Y should be of the same type as the classification used to train ens, and its number of elements should equal the number of rows of tbl or X.

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.


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

Default: 1:NumTrained


Loss function, specified as the comma-separated pair consisting of 'LossFun' and a built-in, loss-function name or function handle.

  • The following table lists the available loss functions. Specify one using its corresponding character vector or string scalar.

    'binodeviance'Binomial deviance
    'classiferror'Classification error
    'mincost'Minimal expected misclassification cost (for classification scores that are posterior probabilities)

    'mincost' is appropriate for classification scores that are posterior probabilities.

    • Bagged and subspace ensembles return posterior probabilities by default (ens.Method is 'Bag' or 'Subspace').

    • If the ensemble method is 'AdaBoostM1', 'AdaBoostM2', GentleBoost, or 'LogitBoost', then, to use posterior probabilities as classification scores, you must specify the double-logit score transform by entering

      ens.ScoreTransform = 'doublelogit';

    • For all other ensemble methods, the software does not support posterior probabilities as classification scores.

  • Specify your own function using function handle notation.

    Suppose that n be the number of observations in X and K be the number of distinct classes (numel(ens.ClassNames), ens is the input model). Your function must have this signature

    lossvalue = lossfun(C,S,W,Cost)

    • The output argument lossvalue is a scalar.

    • You choose the function name (lossfun).

    • C is an n-by-K logical matrix with rows indicating which class the corresponding observation belongs. The column order corresponds to the class order in ens.ClassNames.

      Construct C by setting C(p,q) = 1 if observation p is in class q, for each row. Set all other elements of row p to 0.

    • S is an n-by-K numeric matrix of classification scores. The column order corresponds to the class order in ens.ClassNames. S is a matrix of classification scores, similar to the output of predict.

    • W is an n-by-1 numeric vector of observation weights. If you pass W, the software normalizes them to sum to 1.

    • Cost is a K-by-K numeric matrix of misclassification costs. For example, Cost = ones(K) - eye(K) specifies a cost of 0 for correct classification, and 1 for misclassification.

    Specify your function using 'LossFun',@lossfun.

For more details on loss functions, see Classification Loss.

Default: 'classiferror'


Meaning of the output L:

  • 'ensemble'L is a scalar value, the loss for the entire ensemble.

  • 'individual'L is a vector with one element per trained learner.

  • 'cumulative'L is a vector in which element J is obtained by using learners 1:J from the input list of learners.

Default: 'ensemble'


A logical matrix of size N-by-T, where:

  • N is the number of rows of X.

  • T is the number of weak learners in ens.

When UseObsForLearner(i,j) is true, learner j is used in predicting the class of row i of X.

Default: true(N,T)


Vector of observation weights, with nonnegative entries. The length of weights must equal the number of rows in X. When you specify weights, loss normalizes the weights so that observation weights in each class sum to the prior probability of that class.

Default: ones(size(X,1),1)

Output Arguments


Classification loss, by default the fraction of misclassified data. L can be a vector, and can mean different things, depending on the name-value pair settings.


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Load Fisher's iris data set.

load fisheriris

Train a classification ensemble of 100 decision trees using AdaBoostM2. Specify tree stumps as the weak learners.

t = templateTree('MaxNumSplits',1);
ens = fitcensemble(meas,species,'Method','AdaBoostM2','Learners',t);

Estimate the classification error of the model using the training observations.

L = loss(ens,meas,species)
L = 0.0333

Alternatively, if ens is not compact, then you can estimate the training-sample classification error by passing ens to resubLoss.

More About

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Extended Capabilities

See Also

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