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I'm new with machine learning, I have labeled data with 0 for normal and 1 for attack, The idea is like this:

I want to build a model that apply DT in the first level. The output from DT will be Either Normal or attack. Firstly if the data classified as attack by DT we alert, Secondly, if the data classified as a normal, we take the normal data and fed it to the second model (SVM) to double check if normal or attack.

I have read about ensemble learning, but most of these methods combine the models and take the average or weighting, Any idea how can we implement this? Thanks

M.Python
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1 Answers1

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You defined a rule for combining the two models, but why don’t you let a model learn this rule?

You can train both DT and SVM on all the data. Then, take the output of both (the probability of normal), and feed this to another model (a DT for example) that will predict the final prediction.

In this way the last model (which is called super model), can learn if the right way to combine the models is average/weighting/max/min or your rule...

I hope it helps

theletz
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