RegressionEnsemble

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 ([]).

CombineWeights

A character vector describing how the ensemble combines learner predictions.

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

A numeric array of fit information. The FitInfoDescription property describes the content of this array.

FitInfoDescription

Character vector describing the meaning of the FitInfo array.

LearnerNames

Cell array of character vectors with names of the 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'}.

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:

Method

A character vector with the name of the algorithm fitrensemble used for training the ensemble.

ModelParameters

Parameters used in training ens.

NumObservations

Numeric scalar containing the number of observations in the training data.

NumTrained

Number of trained learners in the ensemble, a positive scalar.

PredictorNames

A cell array of names for the predictor variables, in the order in which they appear in X.

ReasonForTermination

A character vector describing the reason fitrensemble stopped adding weak learners to the ensemble.

Regularization

A structure containing the result of the regularize method. Use Regularization with shrink to lower resubstitution error and shrink the ensemble.

ResponseName

A character vector with the name of the response variable Y.

ResponseTransform

Function handle for transforming scores, or character vector representing a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x.

Add or change a ResponseTransform function using dot notation:

ens.ResponseTransform = @function

Trained

The trained learners, a cell array of compact regression models.

TrainedWeights

A numeric vector of weights the ensemble assigns to its learners. The ensemble computes predicted response by aggregating weighted predictions from its learners.

W

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

X

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

Y

The numeric column vector with the same number of rows as X that trained the ensemble. Each entry in Y is the response to the data in the corresponding row of X.