CompactRegressionEnsemble

Package: classreg.learning.regr

Compact regression ensemble class

Description

Compact version of a regression ensemble (of class RegressionEnsemble). The compact version does not include the data for training the regression ensemble. Therefore, you cannot perform some tasks with a compact regression ensemble, such as cross validation. Use a compact regression ensemble for making predictions (regressions) of new data.

Construction

ens = compact(fullEns) constructs a compact decision ensemble from a full decision ensemble.

Input Arguments

fullEns

A regression ensemble created by fitrensemble.

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 ([]).
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.
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.
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.

Object Functions

gather	Gather properties of Statistics and Machine Learning Toolbox object from GPU
lime	Local interpretable model-agnostic explanations (LIME)
loss	Regression error
partialDependence	Compute partial dependence
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predict	Predict responses using ensemble of regression models
predictorImportance	Estimates of predictor importance for regression ensemble
removeLearners	Remove members of compact regression ensemble
shapley	Shapley values

Copy Semantics

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

Examples

Reduce Size of Regression Ensemble

Create a compact regression ensemble for efficiently making predictions on new data.

Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).

load carsmall
X = [Weight Cylinders];
Y = MPG;

Train a boosted ensemble of 100 regression trees using the LSBoost. Specify that Cylinders is a categorical variable.

Mdl = fitrensemble(X,Y,'PredictorNames',{'W','C'},...
    'CategoricalPredictors',2)

Mdl = 
  RegressionEnsemble
           PredictorNames: {'W'  'C'}
             ResponseName: 'Y'
    CategoricalPredictors: 2
        ResponseTransform: 'none'
          NumObservations: 94
               NumTrained: 100
                   Method: 'LSBoost'
             LearnerNames: {'Tree'}
     ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
                  FitInfo: [100x1 double]
       FitInfoDescription: {2x1 cell}
           Regularization: []


  Properties, Methods

Mdl is a RegressionEnsemble model object that contains the training data, among other things.

Create a compact version of Mdl.

CMdl = compact(Mdl)

CMdl = 
  CompactRegressionEnsemble
           PredictorNames: {'W'  'C'}
             ResponseName: 'Y'
    CategoricalPredictors: 2
        ResponseTransform: 'none'
               NumTrained: 100


  Properties, Methods

CMdl is a CompactRegressionEnsemble model object. CMdl is almost the same as Mdl. One exception is that CMdl does not store the training data.

Compare the amounts of space consumed by Mdl and CMdl.

mdlInfo = whos('Mdl');
cMdlInfo = whos('CMdl');
[mdlInfo.bytes cMdlInfo.bytes]

ans = 1×2

      514824      489348

Mdl consumes more space than CMdl.

CMdl.Trained stores the trained regression trees (CompactRegresionTree model objects) that compose Mdl.

Display a graph of the first tree in the compact ensemble.

view(CMdl.Trained{1},'Mode','graph');

By default, fitrensemble grows shallow trees for boosted ensembles of trees.

Predict the fuel economy of a typical car using the compact ensemble.

typicalX = [mean(X(:,1)) mode(X(:,2))];
predMeanX = predict(CMdl,typicalX)

predMeanX = 26.2520

Tips

For a compact ensemble of regression trees, the Trained property of ens stores a cell vector of ens.NumTrained CompactRegressionTree 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 RegressionEnsemble 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 fitrensemble, the following restrictions apply.

  • The value of the ResponseTransform name-value argument cannot be an anonymous function.

  • Code generation limitations for regression trees also apply to ensembles of regression trees. 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 fitrensemble, the value of the ResponseTransform name-value argument must be 'none' (default).

  • 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.

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• The following object functions fully support GPU arrays:

  • gather

  • predictorImportance

  • removeLearners

• The following object functions offer limited support for GPU arrays:

  • loss

  • partialDependence

  • plotPartialDependence

  • predict

• The object functions execute on a GPU if any of the following apply:

  • The model was fitted with GPU arrays.

  • The predictor data that you pass to the object function is a GPU array.

  • The response data that you pass to the object function is a GPU array.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a