Replacing missing values with an arbitrary number
Arbitrary number imputation consists of replacing missing data with an arbitrary value. Commonly used values include 999, 9999, or -1 for positive distributions. This method is suitable for numerical variables. For categorical variables, the equivalent method is to replace missing data with an arbitrary string, as described in the Imputing categorical variables recipe.
When replacing missing values with arbitrary numbers, we need to be careful not to select a value close to the mean, the median, or any other common value of the distribution.
Tip
Arbitrary number imputation can be used when data is not missing at random, when we are building non-linear models, and when the percentage of missing data is high. This imputation technique distorts the original variable distribution.
In this recipe, we will impute missing data with arbitrary numbers using pandas, scikit-learn, and feature-engine.
How to do it...
Let’...
