The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here

Best Regression type to Impute missing values

sunnyalsunnyal Member Posts: 44 Contributor II
edited June 2019 in Help

I have lot of numerical data that is missing atleast 20+ columns, what type of regression model would work bet to impute those missing values?? I have tried KNN, but results doesnt seem good.??

Tagged:

Answers

  • sunnyalsunnyal Member Posts: 44 Contributor II

    Also, Under Impute missing values operator when I check "learn on complete cases". I get error stating that the "Example set is empty" . Perhaps I do not have a complete (all values) for any rows of data?? is it really looking at a complete ROW or Column in the dataset? Not sure how to avoid this error. But when i uncheck this the model works and takes ages to output results..

     

    Any insight on this is greatly appreicated.

     

     

  • Telcontar120Telcontar120 RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn

    Yes, complete cases means no missings at all.  So if it tells you that exampleset is empty, it means you don't have anyone with all non-missings!

    It sounds like you have a lot of missing data, and that can cause serious problems.  Do you have some attributes that are more populated than others?  You may want to consider narrowing down your modeling attributes to those which are only missing in a few cases.

    Without a data sample it is hard to be more specific about which method would work best.  

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
Sign In or Register to comment.