Stacking implementation of 2 neural nets

• Stacking is an ensemble machine learning algorithm that learns how to best combine the predictions from multiple well-performing machine learning models.

so if I use patternnet or lvqnet to create 2 separate net models in the following case : 2 factors of one being ( eg 1: - heart rate and blood profile : which are to be utilised into 1 single decision to decide cholesterol profile of patient, eg 2:- leaf spectra and bark spectra of a single tree : which are to be utilised for its stress diagnosis) .

so then how to stack the 2 nets as per ensemble modeling using Matlab?

Answer 1

[Akshat]

Hi Yogini,

As per what I understand, stacking can be done with two or more machine learning models to make one ensemble model, which outperforms (usually) the models constituting it.

The catch here is, those models are trained on the same training set, but as you mentioned in the question, you will train one neural net on heart rate and the other on blood profile. I am not exactly sure if this will work, as the data used to train isn’t consistent for both the models (even when the dimensions are consistent).

In case you want to stack two models trained on the same dataset, you can follow the steps mentioned in this documentation page:

https://in.mathworks.com/help/stats/stacked-ensemble-stacking-example.html

This is where the documentation made the base models:

% SVM with Gaussian kernel 

rng('default') % For reproducibility 

mdls{1} = fitcsvm(adultdata,'salary','KernelFunction','gaussian', ... 

    'Standardize',true,'KernelScale','auto'); 

 

% SVM with polynomial kernel 

rng('default') 

mdls{2} = fitcsvm(adultdata,'salary','KernelFunction','polynomial', ... 

    'Standardize',true,'KernelScale','auto'); 

 

% Decision tree 

rng('default') 

mdls{3} = fitctree(adultdata,'salary'); 

 

% Naive Bayes 

rng('default') 

mdls{4} = fitcnb(adultdata,'salary'); 

 

% Ensemble of decision trees 

rng('default') 

mdls{5} = fitcensemble(adultdata,'salary'); 

Following is how you stack the models:

rng('default') % For reproducibility 

N = numel(mdls); 

Scores = zeros(size(adultdata,1),N); 

cv = cvpartition(adultdata.salary,"KFold",5); 

for ii = 1:N 

    m = crossval(mdls{ii},'cvpartition',cv); 

    [~,s] = kfoldPredict(m); 

    Scores(:,ii) = s(:,m.ClassNames=='<=50K'); 

end