RegressionEnsemble - Ensemble regression - MATLAB (original) (raw)

Description

RegressionEnsemble 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.

Creation

Create a regression ensemble object using fitrensemble.

Properties

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Ensemble Properties

This property is read-only.

Method used to combine weak learner weights, returned as either'WeightedAverage' or 'WeightedSum'.

Data Types: char

This property is read-only.

Fit information, returned as a numeric array. TheFitInfoDescription property describes the content of this array.

Data Types: double

This property is read-only.

Description of the information in FitInfo, returned as a character vector or cell array of character vectors.

Data Types: char | cell

This property is read-only.

Names of weak learners in the ensemble, returned as a cell array of character vectors. The name of each learner appears just once. For example, if you have an ensemble of 100 trees, LearnerNames is {'Tree'}.

Data Types: cell

This property is read-only.

Method used by fitrensemble to create the ensemble, returned as a character vector.

Data Types: char

This property is read-only.

Parameters used in training the ensemble, returned as anEnsembleParams object. The properties ofModelParameters include the type of ensemble, either'classification' or 'regression', theMethod used to create the ensemble, and other parameters, depending on the ensemble.

This property is read-only.

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

Data Types: double

This property is read-only.

Reason the fitrensemble function stopped adding weak learners to the ensemble, returned as a character vector.

Data Types: char

This property is read-only.

Result of using the regularize object function on the ensemble, returned as a structure. Use Regularization with shrink to lower the resubstitution error and shrink the ensemble.

Data Types: struct

This property is read-only.

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

Data Types: cell

This property is read-only.

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

Data Types: double

Predictor Properties

This property is read-only.

Data Types: cell

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

This property is read-only.

Expanded predictor names, returned as a cell array of character vectors.

If the model uses encoding for categorical variables, thenExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same asPredictorNames.

Data Types: cell

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

This property is read-only.

Predictor values, returned as a real matrix or table. Each column ofX represents one variable (predictor), and each row represents one observation.

Data Types: double | table

Response Properties

This property is read-only.

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

Data Types: char

Data Types: char | string | function_handle

This property is read-only.

Class labels corresponding to the observations in X, returned as a categorical array, cell array of character vectors, character array, logical vector, or numeric vector. Each row of Y represents the classification of the corresponding row of X.

Other Data Properties

This property is read-only.

Number of observations in the training data, returned as a positive integer.NumObservations can be less than the number of rows of input data when there are missing values in the input data or response data.

Data Types: double

This property is read-only.

Scaled weights in tree, returned as a numeric vector.W has length n, the number of rows in the training data.

Data Types: double

Object Functions

compact	Reduce size of machine learning model
crossval	Cross-validate machine learning model
cvshrink	Cross-validate pruning and regularization of regression ensemble
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
regularize	Find optimal weights for learners in regression ensemble
removeLearners	Remove members of compact regression ensemble
resubLoss	Resubstitution loss for regression ensemble model
resubPredict	Predict response of regression ensemble by resubstitution
resume	Resume training of regression ensemble model
shapley	Shapley values
shrink	Prune regression ensemble

Examples

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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 method. Specify that Cylinders is a categorical variable.

Mdl = fitrensemble(X,Y,'Method','LSBoost',... '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: [100×1 double] FitInfoDescription: {2×1 cell} Regularization: []

Properties, Methods

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

Mdl.Trained is the property that stores a 100-by-1 cell vector of the trained regression trees (CompactRegressionTree model objects) that compose the ensemble.

Plot a graph of the first trained regression tree.

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

Figure Regression tree viewer contains an axes object and other objects of type uimenu, uicontrol. The axes object contains 36 objects of type line, text. One or more of the lines displays its values using only markers

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

Predict the fuel economy of 4,000 pound cars with 4, 6, and 8 cylinders.

XNew = [4000*ones(3,1) [4; 6; 8]]; mpgNew = predict(Mdl,XNew)

mpgNew = 3×1

19.5926 18.6388 15.4810

Tips

For an ensemble of regression trees, the Trained property contains a cell vector of ens.NumTrained CompactRegressionTree model objects. For a textual or graphical display of tree t in the cell vector, enter

Extended Capabilities

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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 theCategoricalPredictors 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.

Usage notes and limitations:

The following object functions fully support GPU arrays:
- compact
- crossval
- cvshrink
- gather
- predictorImportance
- regularize
- removeLearners
- resume
- shrink
The following object functions offer limited support for GPU arrays:
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