ClassificationEnsemble

Ensemble classifier

Description

ClassificationEnsemble combines a set of trained weak learner models and data on which these learners were trained. It can predict ensemble response for new data by aggregating predictions from its weak learners. It stores data used for training, can compute resubstitution predictions, and can resume training if desired.

Creation

Description

Create a classification ensemble object (ens) using fitcensemble.

Properties

BinEdges — Bin edges for numeric predictors

cell array of p numeric vectors

This property is read-only.

Bin edges for numeric predictors, specified as a cell array of p numeric vectors, where p is the number of predictors. Each vector includes the bin edges for a numeric predictor. The element in the cell array for a categorical predictor is empty because the software does not bin categorical predictors.

The software bins numeric predictors only if you specify the 'NumBins' name-value argument as a positive integer scalar when training a model with tree learners. The BinEdges property is empty if the 'NumBins' value is empty (default).

You can reproduce the binned predictor data Xbinned by using the BinEdges property of the trained model mdl.

X = mdl.X; % Predictor data
Xbinned = zeros(size(X));
edges = mdl.BinEdges;
% Find indices of binned predictors.
idxNumeric = find(~cellfun(@isempty,edges));
if iscolumn(idxNumeric)
    idxNumeric = idxNumeric';
end
for j = idxNumeric 
    x = X(:,j);
    % Convert x to array if x is a table.
    if istable(x) 
        x = table2array(x);
    end
    % Group x into bins by using the discretize function.
    xbinned = discretize(x,[-inf; edges{j}; inf]); 
    Xbinned(:,j) = xbinned;
end

Xbinned contains the bin indices, ranging from 1 to the number of bins, for numeric predictors. Xbinned values are 0 for categorical predictors. If X contains NaNs, then the corresponding Xbinned values are NaNs.

CategoricalPredictors — Indices of categorical predictors

vector of positive integers | []

This property is read-only.

Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

Data Types: single | double

ClassNames — List of elements in Y with duplicates removed

categorical array | cell array of character vectors | character array | logical vector | numeric vector

This property is read-only.

List of the elements in Y with duplicates removed, returned as a categorical array, cell array of character vectors, character array, logical vector, or a numeric vector. ClassNames has the same data type as the data in the argument Y. (The software treats string arrays as cell arrays of character vectors.)

Data Types: double | logical | char | cell | categorical

CombineWeights — How the ensemble combines weak learner weights

'WeightedAverage' | 'WeightedSum'

This property is read-only.

How the ensemble combines weak learner weights, returned as either 'WeightedAverage' or 'WeightedSum'.

Data Types: char

Cost — Cost of classifying a point into class j when its true class is i

square matrix

Cost of classifying a point into class j when its true class is i, returned as a square matrix. The rows of Cost correspond to the true class and the columns correspond to the predicted class. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames. The number of rows and columns in Cost is the number of unique classes in the response.

Data Types: double

ExpandedPredictorNames — Expanded predictor names

cell array of character vectors

This property is read-only.

Expanded predictor names, returned as a cell array of character vectors.

If the model uses encoding for categorical variables, then ExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same as PredictorNames.

Data Types: cell

FitInfo — Fit information

numeric array

Fit information, returned as a numeric array. The FitInfoDescription property describes the content of this array.

Data Types: double

FitInfoDescription — Description of information in FitInfo

character vector | cell array of character vectors

Description of the information in FitInfo, returned as a character vector or cell array of character vectors.

Data Types: char | cell

HyperparameterOptimizationResults — Description of cross-validation optimization of hyperparameters

BayesianOptimization object | table of hyperparameters and associated values

This property is read-only.

Description of the cross-validation optimization of hyperparameters, returned as a BayesianOptimization object or a table of hyperparameters and associated values. Nonempty when the OptimizeHyperparameters name-value pair is nonempty at creation. Value depends on the setting of the HyperparameterOptimizationOptions name-value pair at creation:

• 'bayesopt' (default) — Object of class BayesianOptimization

• 'gridsearch' or 'randomsearch' — Table of hyperparameters used, observed objective function values (cross-validation loss), and rank of observations from lowest (best) to highest (worst)

LearnerNames — Names of weak learners in ensemble

cell array of character vectors

This property is read-only.

Names of weak learners in ensemble, returned as a cell array of character vectors. The name of each learner appears just once. For example, if you have an ensemble of 100 trees, LearnerNames is {'Tree'}.

Data Types: cell

Method — Method that creates ensemble

character vector

Method that fitcensemble uses to create the ensemble, returned as a character vector.

Example: 'AdaBoostM1'

Data Types: char

ModelParameters — Parameters used in training ensemble

EnsembleParams object

Parameters used in training the ensemble, returned as an EnsembleParams object. The properties of ModelParameters include the type of ensemble, either 'classification' or 'regression', the Method used to create the ensemble, and other parameters, depending on the ensemble.

NumObservations — Number of observations in the training data

positive integer

This property is read-only.

Number of observations in the training data, returned as a positive integer. NumObservations can be less than the number of rows of input data when there are missing values in the input data or response data.

Data Types: double

NumTrained — Number of trained weak learners

positive integer

This property is read-only.

Number of trained weak learners in the ensemble, returned as a positive integer.

Data Types: double

PredictorNames — Predictor names

cell array of character vectors

This property is read-only.

Predictor names, specified as a cell array of character vectors. The order of the entries in PredictorNames is the same as in the training data.

Data Types: cell

Prior — Prior probabilities for each class

m-element vector

Prior probabilities for each class, returned as an m-element vector, where m is the number of unique classes in the response. The order of the elements of Prior corresponds to the order of the classes in ClassNames.

Data Types: double

ReasonForTermination — Reason that fitcensemble stopped adding weak learners to ensemble

character vector

Reason that fitcensemble stopped adding weak learners to the ensemble, returned as a character vector.

Example: 'Terminated normally after completing the requested number of training cycles.'

Data Types: char

ResponseName — Name of the response variable

character vector

This property is read-only.

Name of the response variable, returned as a character vector.

Data Types: char

RowsUsed — Rows of the original predictor data X used for fitting

logical vector

This property is read-only.

Rows of the original predictor data X used for fitting, returned as an n-element logical vector, where n is the number of rows of X. If the software uses all rows of X for constructing the object, then RowsUsed is an empty array ([]).

Data Types: logical

ScoreTransform — Function for transforming scores

function handle | name of a built-in transformation function | 'none'

Function for transforming scores, specified as a function handle or the name of a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x. For a list of built-in transformation functions and the syntax of custom transformation functions, see fitctree.

Add or change a ScoreTransform function using dot notation:

ctree.ScoreTransform = 'function'
% or
ctree.ScoreTransform = @function

Data Types: char | string | function_handle

Trained — Trained classification models

cell vector

Trained classification models, returned as a cell vector. The entries of the cell vector contain the corresponding compact classification models.

Data Types: cell

TrainedWeights — Trained weak learner weights

numeric vector

This property is read-only.

Trained weights for the weak learners in the ensemble, returned as a numeric vector. TrainedWeights has T elements, where T is the number of weak learners in learners. The ensemble computes predicted response by aggregating weighted predictions from its learners.

Data Types: double

UsePredForLearner — Indicator that learner j uses predictor i

logical matrix

Indicator that learner j uses predictor i, returned as a logical matrix of size P-by-NumTrained, where P is the number of predictors (columns) in the training data. UsePredForLearner(i,j) is true when learner j uses predictor i, and is false otherwise. For each learner, the predictors have the same order as the columns in the training data.

If the ensemble is not of type Subspace, all entries in UsePredForLearner are true.

Data Types: logical

W — Scaled weights in ensemble

numeric vector

This property is read-only.

Scaled weights in the ensemble, returned as a numeric vector. W has length n, the number of rows in the training data. The sum of the elements of W is 1.

Data Types: double

X — Predictor values

real matrix | table

This property is read-only.

Predictor values, returned as a real matrix or table. Each column of X represents one variable (predictor), and each row represents one observation.

Data Types: double | table

Y — Class labels

categorical array | cell array of character vectors | character array | logical vector | numeric vector

This property is read-only.

Class labels corresponding to the observations in X, returned as a categorical array, cell array of character vectors, character array, logical vector, or a numeric vector. Each row of Y represents the classification of the corresponding row of X.

Data Types: single | double | logical | char | string | cell | categorical

Object Functions

compact	Reduce size of classification ensemble model
compareHoldout	Compare accuracies of two classification models using new data
crossval	Cross-validate machine learning model
edge	Classification edge for classification ensemble model
gather	Gather properties of Statistics and Machine Learning Toolbox object from GPU
lime	Local interpretable model-agnostic explanations (LIME)
loss	Classification loss for classification ensemble model
margin	Classification margins for classification ensemble model
partialDependence	Compute partial dependence
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predict	Predict labels using classification ensemble model
predictorImportance	Estimates of predictor importance for classification ensemble of decision trees
resubEdge	Resubstitution classification edge for classification ensemble model
resubLoss	Resubstitution classification loss for classification ensemble model
resubMargin	Resubstitution classification margins for classification ensemble model
resubPredict	Classify observations in classification ensemble by resubstitution
resume	Resume training of classification ensemble model
shapley	Shapley values
testckfold	Compare accuracies of two classification models by repeated cross-validation

Examples

Train Boosted Classification Ensemble

Load the ionosphere data set.

load ionosphere

Train a boosted ensemble of 100 classification trees using all measurements and the AdaBoostM1 method.

Mdl = fitcensemble(X,Y,'Method','AdaBoostM1')

Mdl = 
  ClassificationEnsemble
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 351
               NumTrained: 100
                   Method: 'AdaBoostM1'
             LearnerNames: {'Tree'}
     ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
                  FitInfo: [100x1 double]
       FitInfoDescription: {2x1 cell}

Mdl is a ClassificationEnsemble model object.

Mdl.Trained is the property that stores a 100-by-1 cell vector of the trained classification trees (CompactClassificationTree model objects) that compose the ensemble.

Plot a graph of the first trained classification tree.

view(Mdl.Trained{1},'Mode','graph')

By default, fitcensemble grows shallow trees for boosted ensembles of trees.

Predict the label of the mean of X.

predMeanX = predict(Mdl,mean(X))

predMeanX = 1x1 cell array
    {'g'}

Tips

For an ensemble of classification trees, the Trained property of ens stores an ens.NumTrained-by-1 cell vector of compact classification models. For a textual or graphical display of tree t in the cell vector, enter:

• view(ens.Trained{t}.CompactRegressionLearner) for ensembles aggregated using LogitBoost or GentleBoost.

• view(ens.Trained{t}) for all other aggregation methods.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• The predict function supports code generation.

• To integrate the prediction of an ensemble into Simulink^®, you can use the ClassificationEnsemble Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^® Function block with the predict function.

• When you train an ensemble by using fitcensemble, the following restrictions apply.

  • The value of the ScoreTransform name-value argument cannot be an anonymous function.

  • Code generation limitations for the weak learners used in the ensemble also apply to the ensemble.

    • For decision tree weak learners, you cannot use surrogate splits; that is, the value of the Surrogate name-value argument must be 'off'.

    • For k-nearest neighbor weak learners, the value of the Distance name-value argument cannot be a custom distance function. The value of the DistanceWeight name-value argument can be a custom distance weight function, but it cannot be an anonymous function.

• For fixed-point code generation, the following additional restrictions apply.

  • When you train an ensemble by using fitcensemble, you must train an ensemble using tree learners, and the ScoreTransform value cannot be 'invlogit'.

  • Categorical predictors (logical, categorical, char, string, or cell) are not supported. You cannot use the CategoricalPredictors name-value argument. To include categorical predictors in a model, preprocess them by using dummyvar before fitting the model.

  • Class labels with the categorical data type are not supported. Both the class label value in the training data (Tbl or Y) and the value of the ClassNames name-value argument cannot be an array with the categorical data type.

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• The following object functions fully support GPU arrays:

  • compact

  • crossval

  • gather

  • predictorImportance

  • resubEdge

  • resubLoss

  • resubMargin

  • resubPredict

  • resume

• The following object functions offer limited support for GPU arrays:

  • compareHoldout

  • edge

  • loss

  • margin

  • partialDependence

  • plotPartialDependence

  • predict

• The object functions execute on a GPU if at least one of the following applies:

  • The model was fitted with GPU arrays.

  • The predictor data that you pass to the object function is a GPU array.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a

R2023b: Model with discriminant analysis weak learners stores observations with missing predictor values

Starting in R2023b, training observations with missing predictor values are included in the X, Y, and W data properties of classification ensemble models with discriminant analysis weak learners. The RowsUsed property indicates the training observations stored in the model, rather than those used for training. Observations with missing predictor values continue to be omitted from the model training process.

In previous releases, the software omitted training observations that contained missing predictor values from the data properties of the model.

R2022a: Cost property stores the user-specified cost matrix

Starting in R2022a, the Cost property stores the user-specified cost matrix, so that you can compute the observed misclassification cost using the specified cost value. The software stores normalized prior probabilities (Prior) and observation weights (W) that do not reflect the penalties described in the cost matrix. To compute the observed misclassification cost, specify the LossFun name-value argument as "classifcost" when you call the loss or resubLoss function.

Note that model training has not changed and, therefore, the decision boundaries between classes have not changed.

For training, the fitting function updates the specified prior probabilities by incorporating the penalties described in the specified cost matrix, and then normalizes the prior probabilities and observation weights. This behavior has not changed. In previous releases, the software stored the default cost matrix in the Cost property and stored the prior probabilities and observation weights used for training in the Prior and W properties, respectively. Starting in R2022a, the software stores the user-specified cost matrix without modification, and stores normalized prior probabilities and observation weights that do not reflect the cost penalties. For more details, see Misclassification Cost Matrix, Prior Probabilities, and Observation Weights.

Some object functions use the Cost, Prior, and W properties:

• The loss and resubLoss functions use the cost matrix stored in the Cost property if you specify the LossFun name-value argument as "classifcost" or "mincost".

• The loss and edge functions use the prior probabilities stored in the Prior property to normalize the observation weights of the input data.

• The resubLoss and resubEdge functions use the observation weights stored in the W property.

If you specify a nondefault cost matrix when you train a classification model, the object functions return a different value compared to previous releases.

If you want the software to handle the cost matrix, prior probabilities, and observation weights in the same way as in previous releases, adjust the prior probabilities and observation weights for the nondefault cost matrix, as described in Adjust Prior Probabilities and Observation Weights for Misclassification Cost Matrix. Then, when you train a classification model, specify the adjusted prior probabilities and observation weights by using the Prior and Weights name-value arguments, respectively, and use the default cost matrix.