CompactRegressionEnsemble

Compact regression ensemble

Description

Compact version of a regression ensemble. 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.

Creation

Create a CompactRegressionEnsemble object from a full RegressionEnsemble or RegressionBaggedEnsemble model object by using compact.

Properties

CategoricalPredictors — Indices of categorical predictors

vector of positive integers | []

This property is read-only.

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 ([]).

Data Types: single | double

CombineWeights — How the ensemble combines weak learner weights

'WeightedAverage' | 'WeightedSum'

This property is read-only.

How the ensemble combines weak learner weights, returned as either 'WeightedAverage' or 'WeightedSum'.

Data Types: char

ExpandedPredictorNames — Expanded predictor names

cell array of character vectors

This property is read-only.

Expanded predictor names, returned 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.

Data Types: cell

NumTrained — Number of trained weak learners

positive integer

This property is read-only.

Number of trained weak learners in the ensemble, returned as a positive integer.

Data Types: double

PredictorNames — Predictor names

cell array of character vectors

This property is read-only.

Predictor names, specified as a cell array of character vectors. The order of the entries in PredictorNames is the same as in the training data.

Data Types: cell

ResponseName — Name of the response variable

character vector

This property is read-only.

Name of the response variable, returned as a character vector.

Data Types: char

ResponseTransform — Function for transforming raw response values

"none" (default) | function handle | function name

Function for transforming raw response values, specified as a function handle or function name. The default is "none", which means @(y)y, or no transformation. The function should accept a vector (the original response values) and return a vector of the same size (the transformed response values).

Example: Suppose you create a function handle that applies an exponential transformation to an input vector by using myfunction = @(y)exp(y). Then, you can specify the response transformation as ResponseTransform=myfunction.

Data Types: char | string | function_handle

Trained — Trained regression models

cell vector

Trained regression models, returned as a cell vector. The entries of the cell vector contain the corresponding compact regression models.

If Method is 'LogitBoost' or 'GentleBoost', then the ensemble 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.

Data Types: cell

TrainedWeights — Trained weak learner weights

numeric vector

This property is read-only.

Trained weights for the weak learners in the ensemble, returned as a numeric vector. TrainedWeights has T elements, where T is the number of weak learners in learners. The ensemble computes predicted response by aggregating weighted predictions from its learners.

Data Types: double

Object Functions

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

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: []

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

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

      511341      485035

Mdl consumes more space than CMdl.

CMdl.Trained stores the trained regression trees (CompactRegressionTree 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 at least one of the following applies:

  • 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