TreeBagger parameter tuning for classification

Prashant Kumar answered . 2025-07-11 21:11:01

The following works for me in R2018a. It predicts 'Cylinders' (3 classes) and it calls oobError to get the misclassification rate of the ensemble.

 

load carsmall
Cylinders = categorical(Cylinders);
Mfg = categorical(cellstr(Mfg));
Model_Year = categorical(Model_Year);
X = table(Acceleration,Cylinders,Displacement,Horsepower,Mfg,...
    Model_Year,Weight,MPG);
rng('default'); % For reproducibility

maxMinLS = 20;
minLS = optimizableVariable('minLS',[1,maxMinLS],'Type','integer');
numPTS = optimizableVariable('numPTS',[1,size(X,2)-1],'Type','integer');
hyperparametersRF = [minLS; numPTS];

results = bayesopt(@(params)oobErrRF(params,X),hyperparametersRF,...
    'AcquisitionFunctionName','expected-improvement-plus','Verbose',1);

bestOOBErr = results.MinObjective
bestHyperparameters = results.XAtMinObjective

Mdl = TreeBagger(300,X,'Cylinders','Method','classification',...
    'MinLeafSize',bestHyperparameters.minLS,...
    'NumPredictorstoSample',bestHyperparameters.numPTS);

function oobErr = oobErrRF(params,X)
%oobErrRF Trains random forest and estimates out-of-bag quantile error
%   oobErr trains a random forest of 300 regression trees using the
%   predictor data in X and the parameter specification in params, and then
%   returns the out-of-bag quantile error based on the median. X is a table
%   and params is an array of OptimizableVariable objects corresponding to
%   the minimum leaf size and number of predictors to sample at each node.
randomForest = TreeBagger(300,X,'Cylinders','Method','classification',...
    'OOBPrediction','on','MinLeafSize',params.minLS,...
    'NumPredictorstoSample',params.numPTS);
oobErr = oobError(randomForest, 'Mode','ensemble');
end

 

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