ClassificationBaggedEnsemble

Package: classreg.learning.classif
Superclasses: ClassificationEnsemble

Classification ensemble grown by resampling

Description

ClassificationBaggedEnsemble 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.

Construction

Create a bagged classification ensemble object (ens) using fitcensemble. Set the name-value pair argument 'Method' of fitcensemble to 'Bag' to use bootstrap aggregation (bagging, for example, random forest).

Properties

BinEdges	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	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, categorical vector, 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.
FitInfo	Numeric array of fit information. The FitInfoDescription property describes the content of this array.
FitInfoDescription	Character vector describing the meaning of the FitInfo array.
FResample	Numeric scalar between 0 and 1. FResample is the fraction of training data fitcensemble resampled at random for every weak learner when constructing the ensemble.
HyperparameterOptimizationResults	Description of the cross-validation optimization of hyperparameters, stored 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	Cell array of character vectors with the names of weak learners in the ensemble. The name of each learner appears only once. For example, if you have an ensemble of 100 trees, LearnerNames is {'Tree'}.
Method	Character vector describing the method that creates ens.
ModelParameters	Parameters used in training ens.
NumObservations	Numeric scalar containing the number of observations in the training data.
NumTrained	Number of trained weak learners in ens, a scalar.
PredictorNames	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.
ReasonForTermination	Character vector describing the reason fitcensemble stopped adding weak learners to the ensemble.
Replace	Logical value indicating if the ensemble was trained with replacement (true) or without replacement (false).
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	Trained learners, a cell array of 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.
UseObsForLearner	Logical matrix of size N-by-NumTrained, where N is the number of observations in the training data and NumTrained is the number of trained weak learners. UseObsForLearner(I,J) is true if observation I was used for training learner J, and is false otherwise.
W	Scaled weights, a vector with length n, the number of rows in X. The sum of the elements of W is 1.
X	Matrix or table of predictor values that trained the ensemble. Each column of X represents one variable, and each row represents one observation.
Y	A categorical array, cell array of character vectors, character array, logical vector, or a numeric vector with the same number of rows as X. Each row of Y represents the classification of the corresponding row of X.

Object Functions

compact	Compact classification ensemble
compareHoldout	Compare accuracies of two classification models using new data
crossval	Cross-validate ensemble
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
oobEdge	Out-of-bag classification edge
oobLoss	Out-of-bag classification error
oobMargin	Out-of-bag classification margins
oobPermutedPredictorImportance	Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees
oobPredict	Predict out-of-bag response of ensemble
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
resubEdge	Classification edge by resubstitution
resubLoss	Classification error by resubstitution
resubMargin	Classification margins by resubstitution
resubPredict	Classify observations in ensemble of classification models
resume	Resume training ensemble
shapley	Shapley values
testckfold	Compare accuracies of two classification models by repeated cross-validation

Copy Semantics

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

Examples

Train Bagged Ensemble of Classification Trees

Load the ionosphere data set.

load ionosphere

You can train a bagged ensemble of 100 classification trees using all measurements.

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

fitcensemble uses a default template tree object templateTree() as a weak learner when 'Method' is 'Bag'. In this example, for reproducibility, specify 'Reproducible',true when you create a tree template object, and then use the object as a weak learner.

rng('default') % For reproducibility
t = templateTree('Reproducible',true); % For reproducibiliy of random predictor selections
Mdl = fitcensemble(X,Y,'Method','Bag','Learners',t)

Mdl = 
  ClassificationBaggedEnsemble
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 351
               NumTrained: 100
                   Method: 'Bag'
             LearnerNames: {'Tree'}
     ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
                  FitInfo: []
       FitInfoDescription: 'None'
                FResample: 1
                  Replace: 1
         UseObsForLearner: [351x100 logical]


  Properties, Methods

Mdl is a ClassificationBaggedEnsemble 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 deep decision trees for bagged ensembles.

Estimate the in-sample misclassification rate.

L = resubLoss(Mdl)

L = 0

L is 0, which indicates that Mdl is perfect at classifying the training data.

Tips

For a bagged ensemble of classification trees, the Trained property of ens stores a cell vector of ens.NumTrained CompactClassificationTree model objects. For a textual or graphical display of tree t in the cell vector, enter

view(ens.Trained{t})

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 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.

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) 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, resubLoss, or oobLoss 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, resubLoss, and oobLoss 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, resubEdge, oobLoss, and oobEdge 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 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.