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.

Construction

Create a classification ensemble object using 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, 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.

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 names of weak learners in the ensemble. The name of each learner appears just 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.

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'

ens.ScoreTransform = @function

Trained

A cell vector of trained classification models.

If Method is 'LogitBoost' or 'GentleBoost', then ClassificationEnsemble 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.

Scaled weights, a vector with length n, the number of rows in X. The sum of the elements of W is 1

Matrix or table of predictor values that trained the ensemble. Each column of X represents one variable, and each row represents one observation.

Numeric vector, categorical vector, logical vector, character array, or cell array of character vectors. 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
`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
`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.

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}


  Properties, Methods

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.

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