Question about operator's complexity

RintasponRintaspon Member Posts: 2 Contributor I
edited December 2018 in Help

Hello, I would like to know what are time complexities (Big O) of these predictive operators?

  • k-NN
  • Naive Bayes
  • Decision Tree
  • Neural Net

And if possible, could anyone please give me psuedocode of (or how to inspect) these operators? because I need to know how the operators work with my data, especially the Decision Tree. Thank you.

Best Answers

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    Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761 Unicorn
    Solution Accepted

    Hi @Rintaspon,


    Did you check out mod.rapidminer.com? It allows you to 'guesstimate' how the models behave. Also, there is https://docs.rapidminer.com/studio/operators/ that describes the various operators and algorithms as well. If you really need the code, then you'd have to vist the RapidMiner Github acct for the source code.

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    Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn
    Solution Accepted

    As @Thomas_Ott said, mod.rapiminer.com is a great resource.

    From a quick-and-dirty perspective, I would rank these algorithms as follows in terms of time intensity to complete:

    • Naive Bayes (simple counting and basic math is all that ever happens, it's very efficient)
    • k-NN (lazy algorithm takes little time in training but will be slower when applied to new data)
    • Decision Tree (straightforward solutions with simple splits, also very fast to compute)
    • Neural Net (this is an iterative solution and can be computationally intensive depending on the configuration of number of nodes and hidden layers)

    I hope this helps.


    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • Options
    sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Manager
    Solution Accepted

    hello @Rintaspon - I agree with @Telcontar120 and @Thomas_Ott that mod.rapidminer.com is your go-to for day-to-day general sense of complexity.  If you are looking for a more theoretical approach to determine Big O (e.g. O(n) vs O(mn) etc..), you are going to find this in comp sci / ML textbooks on the subject.  A 30-second google search turned up something like this.


    As for pseudocode, again this is normally found in textbooks.  Mine is from circa 1990 so I'm not sure you want it.  :)




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