ClassificationEnsemble class - Superclasses: CompactClassificationEnsemble

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 also stores data used for training and can compute resubstitution predictions. It can resume training if desired.

Construction

ens = fitensemble(X,Y,method,nlearn,learners) returns an ensemble model that can predict responses to data. The ensemble consists of models listed in learners. For more information on the syntax, see the fitensemble function reference page.

ens = fitensemble(X,Y,method,nlearn,learners,Name,Value) returns an ensemble model with additional options specified by one or more Name,Value pair arguments. For more information on the syntax, see the fitensemble function reference page.

Properties

`CategoricalPredictors`	List of categorical predictors. `CategoricalPredictors` is a numeric vector with indices from `1` to `p`, where `p` is the number of columns of `X`.
`ClassNames`	List of the elements in `Y` with duplicates removed. `ClassNames` can be a numeric vector, vector of categorical variables (nominal or ordinal), logical vector, character array, or cell array of strings. `ClassNames` has the same data type as the data in the argument `Y`.
`CombineWeights`	String 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`.
`FitInfo`	Numeric array of fit information. The `FitInfoDescription` property describes the content of this array.
`FitInfoDescription`	String describing the meaning of the `FitInfo` array.
`LearnerNames`	Cell array of strings 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`	String describing the method that creates `ens`.
`ModelParams`	Parameters used in training `ens`.
`NObservations`	Numeric scalar containing the number of observations in the training data.
`NTrained`	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`	Prior probabilities for each class. `Prior` is a numeric vector whose entries relate to the corresponding `ClassNames` property.
`ReasonForTermination`	String describing the reason `fitensemble` stopped adding weak learners to the ensemble.
`ResponseName`	String with the name of the response variable `Y`.
`ScoreTransform`	Function handle for transforming scores, or string 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 `ClassificationTree.fit`. Add or change a `ScoreTransform` function by dot addressing: 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`.
`UsePredForLearner`	Logical matrix of size `P`-by-`NTrained`, 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`.
`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 of predictor values that trained the ensemble. Each column of `X` represents one variable, and each row represents one observation.
`Y`	Numeric vector, vector of categorical variables (nominal or ordinal), logical vector, character array, or cell array of strings. Each row of `Y` represents the classification of the corresponding row of `X`.

Methods

compact	Compact classification ensemble
crossval	Cross validate ensemble
resubEdge	Classification edge by resubstitution
resubLoss	Classification error by resubstitution
resubMargin	Classification margins by resubstitution
resubPredict	Predict ensemble response by resubstitution
resume	Resume training ensemble

Inherited Methods

edge	Classification edge
loss	Classification error
margin	Classification margins
predict	Predict classification
predictorImportance	Estimates of predictor importance

Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB Programming Fundamentals documentation.

Examples

Construct a boosted classification ensemble for the ionosphere data, using the AdaBoostM1 method:

load ionosphere
ens = fitensemble(X,Y,'AdaBoostM1',100,'Tree')

ens = 
classreg.learning.classif.ClassificationEnsemble:
           PredictorNames: {1x34 cell}
    CategoricalPredictors: []
             ResponseName: 'Response'
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
            NObservations: 351
                 NTrained: 100
                   Method: 'AdaBoostM1'
             LearnerNames: {'Tree'}
     ReasonForTermination: [1x77 char]
                  FitInfo: [100x1 double]
       FitInfoDescription: [2x83 char]

Predict the classification of the mean of X:

ypredict = predict(ens,mean(X))

ypredict = 
    'g'

How To

Nonparametric Supervised Learning

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