Need to Use Random Forest Classifier to Replace MIssing Value in Spreadsheets

wufuturawufutura Member Posts: 38 Contributor I
edited August 2020 in Help
Hi everyone = i have been using =MODE() etc to impute missing values in column arrays.  Sick of this.  Seems unsophisticated, and not much fun, either. A more high level ML guy told me that he uses the Random Forest Classifier to rid himself of all the empty cells (which for me are all just in the form of tons of zeros).
2 QUESTIONS: First, what settings do I use to have the classifier leave the existing data undisturbed data undisturbed with the exception of the the zeros (which I want it to replace in some mysterious fashion far more inteligently than I can understand), and second   

Best Answers

  • wufuturawufutura Member Posts: 38 Contributor I
    Solution Accepted
    sorry guys - i posted this prematurely.  my SECOND QUESTION (typos aside):  is just that i'd like to know if you can recommend the easiest operator diagram i can use to accomplish this pretty simple data prep task?   thanks,  rihard
  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,231 RM Data Scientist
    Solution Accepted
    i think the operator which does the trick for you is 'Impute Missing Values'.
    Best,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
    wufutura
  • wufuturawufutura Member Posts: 38 Contributor I
    Solution Accepted
    thank you so much, Martin!

Answers

  • wufuturawufutura Member Posts: 38 Contributor I
    So Martin - the question I have is how much in the way of "contracting fees" would you charge to get me up and running on both Twitter and AWS?  i already have applied for and received both permissions etc...,  am eager to move forward if the costs are reasonable given that i am a former hedge fund manager (of significant size) and might well be able to provide you with a large amount of business going forward.  - - -Richard
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