RegressionPartitionedEnsemble

Cross-validated regression ensemble

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Description

RegressionPartitionedEnsemble is a set of regression ensembles trained on cross-validated folds. Estimate the quality of classification by cross validation using one or more “kfold” methods: kfoldfun, kfoldLoss, or kfoldPredict. Every “kfold” method uses models trained on in-fold observations to predict response for out-of-fold observations. For example, suppose you cross validate using five folds. In this case, every training fold contains roughly 4/5 of the data and every test fold contains roughly 1/5 of the data. The first model stored in Trained{1} was trained on X and Y with the first 1/5 excluded, the second model stored in Trained{2} was trained on X and Y with the second 1/5 excluded, and so on. When you call kfoldPredict, it computes predictions for the first 1/5 of the data using the first model, for the second 1/5 of data using the second model and so on. In short, response for every observation is computed by kfoldPredict using the model trained without this observation.

Creation

You can create a RegressionPartitionedEnsemble object in two ways:

Properties

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Cross-Validation Properties

This property is read-only.

Name of the cross-validated model, returned as a character vector.

Data Types: char

This property is read-only.

Number of folds in the cross-validated ensemble, returned as a positive integer.

Data Types: double

This property is read-only.

Parameters of the cross-validated ensemble, returned as an object.

This property is read-only.

Number of weak learners used in training each fold of the ensemble, returned as a positive integer.

Data Types: double

This property is read-only.

Partition used in cross-validation, returned as a CVPartition object.

This property is read-only.

The trained learners, returned as a cell array of full ensembles trained on cross-validation folds. Every ensemble is full, meaning it contains its training data and weights.

Data Types: cell

This property is read-only.

The trained learners, returned as a cell array of compact ensembles trained on cross-validation folds.

Data Types: cell

Other Regression Properties

This property is read-only.

Bin edges for numeric predictors, specified as a cell array of p numeric vectors, where p is the number of predictors. Each vector includes the bin edges for a numeric predictor. The element in the cell array for a categorical predictor is empty because the software does not bin categorical predictors.

The software bins numeric predictors only if you specify the NumBins name-value argument as a positive integer scalar when training a model with tree learners. The BinEdges property is empty if the NumBins value is empty (default).

You can reproduce the binned predictor data Xbinned by using the BinEdges property of the trained model mdl.

X = mdl.X; % Predictor data
Xbinned = zeros(size(X));
edges = mdl.BinEdges;
% Find indices of binned predictors.
idxNumeric = find(~cellfun(@isempty,edges));
if iscolumn(idxNumeric)
    idxNumeric = idxNumeric';
end
for j = idxNumeric 
    x = X(:,j);
    % Convert x to array if x is a table.
    if istable(x) 
        x = table2array(x);
    end
    % Group x into bins by using the discretize function.
    xbinned = discretize(x,[-inf; edges{j}; inf]); 
    Xbinned(:,j) = xbinned;
end
Xbinned contains the bin indices, ranging from 1 to the number of bins, for the numeric predictors. Xbinned values are 0 for categorical predictors. If X contains NaNs, then the corresponding Xbinned values are NaNs.

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.

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.

Predictor names in order of their appearance in the predictor data X, specified as a cell array of character vectors. The length of PredictorNames is equal to the number of columns in X.

Data Types: cell

This property is read-only.

Response variable name, specified as a character vector.

Data Types: char

Function for transforming the raw response values (mean squared error), specified as a function handle or 'none'. The default 'none' means no transformation; equivalently, 'none' means @(x)x. A function handle must accept a matrix of response values and return a matrix of the same size.

Add or change a ResponseTransform function using dot notation:

tree.ResponseTransform = @function

Data Types: char | function_handle

This property is read-only.

Scaled weights in the ensemble, returned as a numeric vector. W has length n, the number of rows in the training data. The sum of the elements of W is 1.

Data Types: double

This property is read-only.

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

Data Types: double | table

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.

Data Types: single | double | logical | char | string | cell | categorical

Object Functions

gatherGather properties of Statistics and Machine Learning Toolbox object from GPU
kfoldLossLoss for cross-validated partitioned regression model
kfoldPredictPredict responses for observations in cross-validated regression model
kfoldfunCross-validate function for regression
resumeResume training of cross-validated regression ensemble model

Examples

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Open Live Script

Construct a partitioned regression ensemble, and examine the cross-validation losses for the folds.

Load the carsmall data set.

load carsmall;

Create a subset of variables.

XX = [Cylinders Displacement Horsepower Weight];
YY = MPG;

Construct the ensemble model.

rens = fitrensemble(XX,YY);

Create a cross-validated ensemble from rens.

rng(10,'twister') % For reproducibility
cvrens = crossval(rens);

Examine the cross-validation losses.

L = kfoldLoss(cvrens,'mode','individual')
L = 10×1

   21.4489
   48.4388
   28.2560
   17.5354
   29.9441
   49.5254
   51.2372
   31.0152
   31.6388
    8.9607

L is a vector containing the cross-validation loss for each trained learner in the ensemble.

Extended Capabilities

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Version History

Introduced in R2011a

See Also

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