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Can you impute categorical variables?
2. Replace missing values with the most frequent value: You can always impute them based on Mode in the case of categorical variables, just make sure you don’t have highly skewed class distributions.
How do categorical variables deal with missing values?
How to handle missing values of categorical variables?
- Ignore these observations.
- Replace with general average.
- Replace with similar type of averages.
- Build model to predict missing values.
Can you use multiple imputation for categorical data?
Most Multiple Imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical variables. Impute a continuous value. This will generally be between 0 and 1.
How do I replace Nan in categorical data?
Step 1: Find which category occurred most in each category using mode(). Step 2: Replace all NAN values in that column with that category. Step 3: Drop original columns and keep newly imputed columns.
Which is the best method to impute a variable?
Also since the variable to be imputed is categorical I would prefer to avoid methods that use regression techniques to impute values. You could use random hot deck imputation. Roughly, this is a method where missing values are replaced with values from an observation with “similar” values in the non-missing variables.
How are missing values replaced with similar values?
Roughly, this is a method where missing values are replaced with values from an observation with “similar” values in the non-missing variables. For each missing value, the algorithm generates a pool of similar observations (“donors”) and randomly chooses from them.
How to convert ordered input factors to numeric?
Convert ordered input factors to numeric (tree-based methods work much faster then). A possibility is also to manually dummy code an input factor which greatly reduces computational effort. If the response variable is a factor, then a random forest does classification, not regression.