Arbitrary number imputation consists of replacing missing values with an arbitrary value. Some commonly used values include 999, 9999, or -1 for positive distributions. This method is suitable for numerical variables. A similar method for categorical variables will be discussed in the Capturing missing values in a bespoke category recipe.
When replacing missing values with an arbitrary number, we need to be careful not to select a value close to the mean or the median, or any other common value of the distribution.
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 by arbitrary numbers using pandas, scikit...
