CompactClassificationEnsemble

Package: classreg.learning.classif

Compact classification ensemble class

Description

Compact version of a classification ensemble (of class ClassificationEnsemble). The compact version does not include the data for training the classification ensemble. Therefore, you cannot perform some tasks with a compact classification ensemble, such as cross validation. Use a compact classification ensemble for making predictions (classifications) of new data.

Construction

ens = compact(fullEns) constructs a compact decision ensemble from a full decision ensemble.

Input Arguments

fullEns

A classification ensemble created by fitcensemble.

Properties

CategoricalPredictors	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 ([]).
ClassNames	List of the elements in Y with duplicates removed. ClassNames can be a numeric vector, vector of categorical variables, logical vector, character array, or cell array of character vectors. ClassNames has the same data type as the data in the argument Y. (The software treats string arrays as cell arrays of character vectors.)
CombineWeights	Character vector describing how ens combines weak learner weights, either 'WeightedSum' or 'WeightedAverage'.
Cost	Square matrix, where Cost(i,j) is the cost of classifying a point into class j if its true class is i (the rows 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. This property is read-only.
ExpandedPredictorNames	Expanded predictor names, stored 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.
NumTrained	Number of trained weak learners in ens, a scalar.
PredictorNames	A cell array of names for the predictor variables, in the order in which they appear in X.
Prior	Numeric vector of prior probabilities for each class. The order of the elements of Prior corresponds to the order of the classes in ClassNames. The number of elements of Prior is the number of unique classes in the response. This property is read-only.
ResponseName	Character vector with the name of the response variable Y.
ScoreTransform	Function handle for transforming scores, or character vector representing 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: ens.ScoreTransform = 'function' or ens.ScoreTransform = @function
Trained	A cell vector of trained classification models. If Method is 'LogitBoost' or 'GentleBoost', then CompactClassificationEnsemble stores trained learner j in the CompactRegressionLearner property of the object stored in Trained{j}. That is, to access trained learner j, use ens.Trained{j}.CompactRegressionLearner. Otherwise, cells of the cell vector contain the corresponding, compact classification models.
TrainedWeights	Numeric vector of trained weights for the weak learners in ens. TrainedWeights has T elements, where T is the number of weak learners in learners.
UsePredForLearner	Logical matrix of size P-by-NumTrained, where P is the number of predictors (columns) in the training data X. 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 X. If the ensemble is not of type Subspace, all entries in UsePredForLearner are true.

Object Functions

compareHoldout	Compare accuracies of two classification models using new data
edge	Classification edge
gather	Gather properties of Statistics and Machine Learning Toolbox object from GPU
lime	Local interpretable model-agnostic explanations (LIME)
loss	Classification error
margin	Classification margins
partialDependence	Compute partial dependence
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predict	Classify observations using ensemble of classification models
predictorImportance	Estimates of predictor importance for classification ensemble of decision trees
removeLearners	Remove members of compact classification ensemble
shapley	Shapley values

Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects.

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: [100x1 double]
       FitInfoDescription: {2x1 cell}


  Properties, Methods

Mdl is a ClassificationEnsemble model object that contains the training data, among other things.

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 almost the same as Mdl. One exception is that CMdl does not store the training data.

Compare the amounts of space consumed by Mdl and CMdl.

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

ans = 1×2

      878354      631686

Mdl consumes more space 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 = 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:

  • 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 either of the following apply:

  • 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 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.