Hi everyone,
I have set up different regression type machine learning models (GaussianSVM, medium decision tree, linear robust model showed best accuracy) to predict a target value using identical (numerical) features in all three models. I am now wondering, if and how it is possible to stack models to further increase accuracy. I am thinking of using these three models as first layer and another linear model or SVM for the second layer of the ensemble.
Does it make sense to use different features in the models of the first layer of the ensemble and combine / weight them in the second ensemble layer? Do all features have to be numerical or could one model use classifying features? Where exactly would I find the 'weights' of the first layer models to be used as features in the second layer of the ensemble and how would I incorporate the classification model?
Since I am quite new in ML with no IT background, I would appreciate a simple step by step approach ;o)
Thank you so much!
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