How to know whether to perform any preprocessing or not?

201202010201202010 Member Posts: 1 Newbie
edited November 2018 in Help
Do we have to perform pre-processing when using Random Forest?
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Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 2,114  RM Data Scientist
    Hi,
    well.. The short answer here is: no.

    One of the coolest thing for Random Forests is, that they can basically handle all kind of data and also have some "built-in feature selection". Thus you can just throw data on it and get reasonable results. The only exception for this are date attributes, which you should preprocess (e.g. Day of the Week).

    Now the longer answer: The right preprocessing can get you better results. While Random Forests are easy-care algorithms, you can still do things. One problem could be feature generation to get around XOR-Problems. The new Auto-Model feature for automatic feature generation, which is part of the 9.1 (Beta) can help here.

    BR,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • rfuentealbarfuentealba Moderator, RapidMiner Certified Analyst, Member, University Professor Posts: 399   Unicorn
    Hi @201202010,

    I agree with @mschmitz: You don't have to. Nevertheless, I would pass only the features I want to use to my algorithm, and remove the correlated attributes.
  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,226   Unicorn
    Also, if you are not necessarily going to use Random Forest, then other algorithms will benefit from or require preprocessing.  So it is a helpful step to perform as part of your EDA, especially, handling missings, outliers, etc.
    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
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