How can missingness itself be useful as a feature?

Missingness itself can be useful as a feature when the fact that a value is missing carries information about the outcome being predicted.

In such cases, instead of only filling the missing value, you can also create a separate indicator showing whether the original value was missing.

Simple example

Suppose a loan dataset contains an income feature, but some applicants do not provide their income.

You might create:

income_missing = 1  if income is missing
income_missing = 0  otherwise

Then you can still impute the missing income value while allowing the model to learn whether the missingness itself is informative.

Why missingness can contain information

A value may be missing for a reason.

For example:

• a customer skipped an optional form field
• a medical test was not ordered
• a sensor failed under certain operating conditions
• a user did not complete part of a process

If those situations are related to the target, the missing-value pattern can help the model.

Missing indicator + imputation

A common approach is:

• create a binary missing-value indicator
• impute the missing value using an appropriate method
• give both features to the model

This separates two pieces of information:

• the estimated value used in place of the missing entry
• the fact that the original value was missing

When this may not help

A missing indicator is not automatically useful.

If values are missing completely at random and the missingness has no relationship with the target, the indicator may add little or no predictive value.

It should be evaluated using validation data.

Watch for leakage

Missingness indicators must also respect the prediction-time boundary.

If a value becomes missing only because of an event that happens after the prediction point, using that missingness could leak future information.

In short, missingness can be useful as a feature when the absence of a value carries meaningful information. A separate missing-value indicator lets the model learn from that signal while the original feature is handled through imputation or another method.