CompactClassificationEnsemble class -

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.

NTrained

Number of trained weak learners in cens, a scalar.

PredictorNames

A 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.

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:

cens.ScoreTransform = 'function'

or

cens.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.