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For my final project in control engineering I was tasked with writing a couple of ml algorithms and rank them to suggest the best one. The code I was working on was not good at all and all the data was being processed in Matlab, so I spent all the project refactoring and proposing a viable solution to using python as a math backend for the java application.

Anyways I had to research a little into the subject and what I found is that there isn't a straightforward approach to choosing algorithms. I could be mistaken, but I believe the best approach for you would be to get intimate knowledge from every ml algorithm and maybe use a cheatsheet to guide you, but ultimately only knowing well your data set (distribution of classes, occurrences, which traits are better for which categorization you want to do etc..) will bring you farther than looking for a recipe for choosing an algorithm.




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