Imputing Missing Data
Missing data—meaning the absence of values for certain observations—is an unavoidable problem in most data sources. Some machine learning model implementations can handle missing data out of the box. To train other models, we must remove observations with missing data or transform them into permitted values.
The act of replacing missing data with their statistical estimates is called imputation. The goal of any imputation technique is to produce a complete dataset. There are multiple imputation methods. We select which one to use, depending on whether the data is missing at random, the proportion of missing values, and the machine learning model we intend to use. In this chapter, we will discuss several imputation methods.
This chapter will cover the following recipes:
- Removing observations with missing data
- Performing mean or median imputation
- Imputing categorical variables
- Replacing missing values with an arbitrary number
- Finding extreme values for imputation
- Marking imputed values
- Implementing forward and backward fill
- Carrying out interpolation
- Performing multivariate imputation by chained equations
- Estimating missing data with nearest neighbors
