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How to impute the missing data with the most frequent value
Contributor I
I have a dataset containing categorical and numerical data and I would like to know how I can impute the missing data with the frequent value.
For example
ID Feature1 Feature 2 Feature 3
_______________________________________
123 core i7 Windows 33844690
334 IOS phone 99983648
Note that the missing values are huge but I can't drop the column.
Is there an operator I can use for nominal data to be replaced with the mode? and the numerical data with average or max?
For example
ID Feature1 Feature 2 Feature 3
_______________________________________
123 core i7 Windows 33844690
334 IOS phone 99983648
Note that the missing values are huge but I can't drop the column.
Is there an operator I can use for nominal data to be replaced with the mode? and the numerical data with average or max?
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Best Answer
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MartinLiebig
Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,533 
RM Data Scientist
Hi,you can use Replace Missing Values. Average actually takes the mode for nominal columns.Cheers,Martin- Sr. Director Data Solutions, Altair RapidMiner -
Dortmund, Germany0
