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Let's say that I have a vector of features that I use to get a single result back using some machine learning algorithm.

I thought about using multiple variations of that algorithm to get multiple results back and then simply concatenating them together into one big features vector for a new algorithm to give me that one result back.

Would that enhance accuracy or make it worse?

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This is widely used method, called stacking, see e.g. Bagging, boosting and stacking in machine learning

To answer your question: yes, it is very useful in some cases. If you cook it properly with folding, it almost sure to have quality no worse than a single model.

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