How to re-train a model optimized by Bayesian optimization on new data?

Question

Sebastian 2019-9-20

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/481300-how-to-re-train-a-model-optimized-by-bayesian-optimization-on-new-data

评论： Denys Romanenko 2023-4-1

采纳的回答： Don Mathis

Hi,

I optimized a regression ensemble

model = fitrensemble(X,Y,...);

with active HyperparameterOptimizationOptions, so the resulting model object of type RegressionBaggedEnsemble contains a HyperparameterOptimizationResults object. How can I use this easily to re-train the model on a new data set with the best point found by the hyperparameter optimization algorithm? Something like this would be nice:

newModel = fitrensemble(Xnew,YNew,model.HyperparameterOptimizationResults.bestPoint);

My current approach is tedious, because I have to distinguish between the method the optimization algorithm selected (Bag,LSBoost) and set all parameters manually (and I'm even not sure if this is correct):

best = model.HyperparameterOptimizationResults.bestPoint;
ttmp = templateTree('MinLeafSize',best.MinLeafSize,'MaxNumSplits',...
    best.MaxNumSplits,'NumVariablesToSample',best.NumVariablesToSample);
if best.Method=='Bag' %#ok<BDSCA>
    newModel = fitrensemble(Xnew,YNew,...
        'Method','Bag','Learners',ttmp,'NumLearningCycles',best.NumLearningCycles);
else
    newModel = fitrensemble(Xnew,YNew,...
        'Method','LSBoost','Learners',ttmp,'NumLearningCycles',best.NumLearningCycles,'LearnRate',best.LearnRate);
end

This question is not restricted to fitrensemble, but includes all similar model functions available in Statistics and Machine Learning Toolbox (fitrlinear, fitrgp ...).

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Answer 1

Don Mathis 2019-9-20

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/481300-how-to-re-train-a-model-optimized-by-bayesian-optimization-on-new-data#answer_392824

编辑：Don Mathis 2019-9-20

Your current approach (explicitly passing the values that the optimization found) is the right way to do it.

There is a faster/simpler way which uses undocumented functionality, and which therefore may change in the future. So, use at your own risk. You create a template from your model and then fit it:

model = fitrensemble(X,Y,...);
tmp = classreg.learning.FitTemplate.makeFromModelParams(model.ModelParameters);
newModel = fit(tmp,Xnew,Ynew)

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Sinan Islam 2021-4-24

编辑：Sinan Islam 2021-7-16

@Don Mathisis this still the only way to retrain a model on new data (without using Classification Learner App)? Have MATLAB implemented a more formal method?

Denys Romanenko 2023-4-1

@Don Mathis and @Sebastian: Is there an analogue approach to retrain a fitcecoc - classifier? Unfortunately the above described approach is not applicable.

Thank you!

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