Hello everyone, I have the following question. In general, as a rule of thumb features need to be scaled using min-max scaling or z-score standardization before being used in ML algorithms. However, it is not always possible to perform this procedure (e.g., in cases when you do not have all the data, or you do not have enough resources to perform this operation). At the same time, some classification algorithms do not require data scaling to operate correctly (e.g., Random Forest classifier). Correct me if I am wrong in this assumption.
If it is possible, could you name please classification algorithms that do not require feature scaling and those which require? And one more question. Have you ever seen the comparison of algorithms in terms of their speed and memory consumption (maybe, there is such comparison for algorithms in scikit)? Where I can find the information which algorithms are more greedy and which are not? Sorry if my questions seem to you too basic but I am in the beginning of my way. Thank you in advance!
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