CompactClassificationEnsemble

R2026b

Compact classification ensemble

Description

CompactClassificationEnsemble is a compact version of a classification ensemble. The compact version does not include the data used to train the classification ensemble. Therefore, you cannot perform some tasks, such as cross-validation, using the compact classification ensemble. Use a compact classification ensemble to make predictions (classifications) for new data.

Creation

Create a CompactClassificationEnsemble object from a full ClassificationEnsemble or ClassificationBaggedEnsemble model object by using the compact object function.

Properties

Ensemble Properties

CombineWeights — Method used to combine weak learner weights

Read-only: 'WeightedAverage' | 'WeightedSum'

This property is read-only.

Method used to combine weak learner weights, returned as 'WeightedAverage' or 'WeightedSum'.

Data Types: char

NumTrained — Number of trained weak learners

Read-only: positive integer

This property is read-only.

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

Data Types: double

Trained — Trained weak learners

Read-only: cell vector

This property is read-only.

Trained weak learners, 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

Read-only: numeric vector

This property is read-only.

Trained weak learner weights, returned as a numeric vector. TrainedWeights has NumTrained elements, where NumTrained is the number of weak learners in the ensemble. The ensemble computes the 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

Data Properties

PredictorNames — Predictor names

Read-only: cell array of character vectors

This property is read-only.

Predictor names, returned 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

CategoricalPredictors — Categorical predictor indices

Read-only: vector of positive integers | []

This property is read-only.

Categorical predictor indices, returned 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

ExpandedPredictorNames — Expanded predictor names

Read-only: 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

ClassNames — List of elements in Y with duplicates removed

Read-only: 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 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

ResponseName — Name of response variable

Read-only: character vector

This property is read-only.

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

Data Types: char

Other Classification Properties

Cost — Misclassification costs

Read-only: square numeric matrix

This property is read-only.

Misclassification costs, returned as a square numeric matrix. Cost has K rows and columns, where K is the number of classes.

Cost(i,j) is the cost of classifying a point into class j if its true class is i. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames.

Data Types: double

Prior — Prior probabilities for each class

Read-only: numeric vector

This property is read-only.

Prior probabilities for each class, returned as a K-element numeric vector, where K 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

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 ScoreTransform (for trees) or ScoreTransform (for ensembles).

Add or change a ScoreTransform function using dot notation:

Mdl.ScoreTransform = "function"
% or
Mdl.ScoreTransform = @function

Data Types: char | string | function_handle

Object Functions

compareHoldout	Compare accuracies of two classification models using new data
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
removeLearners	Remove members of compact classification ensemble
shapley	Shapley values

Examples

Reduce Size of Classification Ensemble

Create a compact classification ensemble for efficiently making predictions on new data.

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: [100×1 double]
       FitInfoDescription: {2×1 cell}


  Properties, Methods

Mdl is a ClassificationEnsemble model object that includes the training data.

Create a compact version of Mdl.

CMdl = compact(Mdl)

CMdl = 
  CompactClassificationEnsemble
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
               NumTrained: 100


  Properties, Methods

CMdl is a CompactClassificationEnsemble model object. CMdl is similar to Mdl, but does not store the training data.

Compare the amount of memory used by Mdl and CMdl.

mdlInfo = whos("Mdl");
cMdlInfo = whos("CMdl");
[mdlInfo.bytes cMdlInfo.bytes]

ans = 1×2

      864863      617126

Mdl uses more memory than CMdl.

CMdl.Trained stores the trained classification trees (CompactClassificationTree model objects) that compose Mdl.

Display a graph of the first tree in the compact ensemble.

view(CMdl.Trained{1},Mode="graph");

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

Predict the label of the mean of X using the compact ensemble.

predMeanX = predict(CMdl,mean(X))

predMeanX = 1×1 cell array
    {'g'}

Tips

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

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

  • view(Mdl.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 for Statistics and Machine Learning Functions.

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Refer to the usage notes and limitations in the C/C++ Code Generation section. The same usage notes and limitations apply to GPU 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:

  • gather

  • predictorImportance

  • removeLearners

• 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

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) 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 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 used for training in the Prior property. Starting in R2022a, the software stores the user-specified cost matrix without modification, and stores normalized prior probabilities 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 and Prior properties:

• The loss function uses 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.

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.