Imputation Techinques for Catogorical Data

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Joined: Tue May 09, 2017 3:54 am

Imputation Techinques for Catogorical Data

Postby ravikanth.karedla » Mon Jun 19, 2017 8:29 am

Hi Team,

I've a solved data set and he imputed missing values for Categorical Data, Can you please help me in understanding what are all the techniques used to impute data for Categorical data.

Thanks in advance!
Ravi Kanth Karedla


Finance Junkie
Posts: 947
Joined: Wed Apr 09, 2014 6:28 am

Re: Imputation Techinques for Catogorical Data

Postby edupristine » Fri Jul 21, 2017 9:54 am

Hi Ravi,

A naïve approach usually followed in case if imputing categorical variables is to replace the unknown value by the Mode of data. It is the most simple approach and may result in bias. Below are some techniques that one could use depending on the model.
a) Remove the rows if the instance of missing values is very less compared to data volume.
b) Ignore the categorical variables if the missing values is more than 80% of data.
c) Use a simple approach like replacing with Mode
d) Consider the missing value as a level / category and proceed with modelling
e) Emply techniques like KNN to predict the missing value using other predictor variables in data.
Above techniques hold good for Ordinal and dichotomous variables.
Along with the mentioned approaches, there are specific R packages like Hmisc, mice, VIM, amelia etc which help in imputing missing values.

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