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oobQuantileError

Class: TreeBagger

Out-of-bag quantile loss of bag of regression trees

Description

example

err = quantileError(Mdl) returns half of the out-of-bag mean absolute deviation (MAD) from comparing the true responses in Mdl.Y to the predicted, out-of-bag medians at Mdl.X, the predictor data, and using the bag of regression trees Mdl. Mdl must be a TreeBagger model object.

example

err = quantileError(Mdl,Name,Value) uses additional options specified by one or more Name,Value pair arguments. For example, specify quantile probabilities, the error type, or which trees to include in the quantile-regression-error estimation.

Input Arguments

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Bag of regression trees, specified as a TreeBagger model object created by TreeBagger.

  • The value of Mdl.Method must be regression.

  • When you train Mdl using TreeBagger, you must specify the name-value pair 'OOBPrediction','on'. Consequently, TreeBagger saves required out-of-bag observation index matrix in Mdl.OOBIndices.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Ensemble error type, specified as the comma-separated pair consisting of 'Mode' and a value in this table. Suppose tau is the value of Quantile.

ValueDescription
'cumulative'

err is a Mdl.NumTrees-by-numel(tau) numeric matrix of cumulative quantile regression errors. err(j,k) is the tau(k) quantile regression error using the learners in Mdl.Trees(1:j) only.

'ensemble'

err is a 1-by-numel(tau) numeric vector of cumulative quantile regression errors for the entire ensemble. err(k) is the tau(k) ensemble quantile regression error.

'individual'

err is a Mdl.NumTrees-by-numel(tau) numeric matrix of quantile regression errors from individual learners. err(j,k) is the tau(k) quantile regression error using the learner in Mdl.Trees(j) only.

For 'cumulative' and 'individual', if you choose to include fewer trees in quantile estimation using Trees, then this action affects the number of rows in err and corresponding row indices.

Example: 'Mode','cumulative'

Quantile probability, specified as the comma-separated pair consisting of 'Quantile' and a numeric vector containing values in the interval [0,1]. For each observation (row) in Mdl.X, oobQuantileError estimates corresponding quantiles for all probabilities in Quantile.

Example: 'Quantile',[0 0.25 0.5 0.75 1]

Data Types: single | double

Indices of trees to use in response estimation, specified as the comma-separated pair consisting of 'Trees' and 'all' or a numeric vector of positive integers. Indices correspond to the cells of Mdl.Trees; each cell therein contains a tree in the ensemble. The maximum value of Trees must be less than or equal to the number of trees in the ensemble (Mdl.NumTrees).

For 'all', oobQuantileError uses all trees in the ensemble (that is, the indices 1:Mdl.NumTrees).

Values other than the default can affect the number of rows in err.

Example: 'Trees',[1 10 Mdl.NumTrees]

Data Types: char | string | single | double

Weights to attribute to responses from individual trees, specified as the comma-separated pair consisting of 'TreeWeights' and a numeric vector of numel(trees) nonnegative values. trees is the value of Trees.

If you specify 'Mode','individual', then oobQuantileError ignores TreeWeights.

Data Types: single | double

Output Arguments

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Half of the out-of-bag quantile regression error, returned as a numeric scalar or T-by-numel(tau) matrix. tau is the value of Quantile.

T depends on the values of Mode, Trees, and Quantile. Suppose that you specify 'Quantile',tau and 'Trees',trees.

  • For 'Mode','cumulative', err is a numel(trees)-by-numel(tau) numeric matrix. err(j,k) is the tau(k) cumulative, out-of-bag quantile regression error using the learners in Mdl.Trees(trees(1:j)).

  • For 'Mode','ensemble', err is a 1-by-numel(tau) numeric vector. err(k) is the tau(k) cumulative, out-of-bag quantile regression error using the learners in Mdl.Trees(trees).

  • For 'Mode','individual', err is a numel(trees)-by-numel(tau) numeric matrix. err(j,k) is the tau(k) out-of-bag quantile regression error using the learner in Mdl.Trees(trees(j)).

Examples

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Load the carsmall data set. Consider a model that predicts the fuel economy of a car given its engine displacement, weight, and number of cylinders. Consider Cylinders a categorical variable.

load carsmall
Cylinders = categorical(Cylinders);
X = table(Displacement,Weight,Cylinders,MPG);

Train an ensemble of bagged regression trees using the entire data set. Specify 100 weak learners and save the out-of-bag indices.

rng(1); % For reproducibility
Mdl = TreeBagger(100,X,'MPG','Method','regression','OOBPrediction','on');

Mdl is a TreeBagger ensemble.

Perform quantile regression, and out-of-bag estimate the MAD of the entire ensemble using the predicted conditional medians.

oobErr = oobQuantileError(Mdl)
oobErr = 1.5349

oobErr is an unbiased estimate of the quantile regression error for the entire ensemble.

Load the carsmall data set. Consider a model that predicts the fuel economy of a car given its engine displacement, weight, and number of cylinders.

load carsmall
X = table(Displacement,Weight,Cylinders,MPG);

Train an ensemble of bagged regression trees using the entire data set. Specify 250 weak learners and save the out-of-bag indices.

rng('default'); % For reproducibility
Mdl = TreeBagger(250,X,'MPG','Method','regression',...
    'OOBPrediction','on');

Estimate the cumulative; out-of-bag; 0.25, 0.5, and 0.75 quantile regression errors.

err = oobQuantileError(Mdl,'Quantile',[0.25 0.5 0.75],'Mode','cumulative');

err is an 250-by-3 matrix of cumulative, out-of-bag, quantile regression errors. Columns correspond to quantile probabilities and rows correspond to trees in the ensemble. The errors are cumulative, so they incorporate aggregated predictions from previous trees.

Plot the cumulative, out-of-bag, quantile errors on the same plot.

figure;
plot(err);
legend('0.25 quantile error','0.5 quantile error','0.75 quantile error');
ylabel('Out-of-bag quantile error');
xlabel('Tree index');
title('Cumulative, Out-of-Bag, Quantile Regression Error')

All quantile error curves appear to level off after training about 50 trees. So, training 50 trees appears to be sufficient to achieve minimal quantile error for the three quantile probabilities.

More About

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Tips

The out-of-bag ensemble error estimator is unbiased for the true ensemble error. So, to tune parameters of a random forest, estimate the out-of-bag ensemble error instead of implementing cross-validation.

References

[1] Breiman, L. Random Forests. Machine Learning 45, pp. 5–32, 2001.

[2] Meinshausen, N. “Quantile Regression Forests.” Journal of Machine Learning Research, Vol. 7, 2006, pp. 983–999.

Introduced in R2016b