A missing indicator is a binary feature that records whether the original value was absent. Imputation supplies a usable numeric/categorical value; the indicator lets the model learn whether the fact of being missing itself carries information.
A missing indicator is a binary feature that records whether the original value was absent. Imputation supplies a usable numeric/categorical value; the indicator lets the model learn whether the fact of being missing itself carries information.
The practical value of Missing-indicator features comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.
A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.
Create `feature_was_missing = feature.isna()` before or as part of the imputation transformer, then include the indicator in the model input.
The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.
A lab test is often missing for healthier patients because clinicians did not order it. The missing flag may therefore carry clinical workflow information.
A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.
Keep the example small enough that you can inspect each stage manually.
# Purpose: demonstrate Missing-indicator features with a small, inspectable example.
# Follow the comments and printed stages to connect each operation with its result.
# Import the library or helper used in this example.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import numpy as np
# Import the library or helper used in this example.
# Step 2 — Import the module so its functions/classes are available to the rest of this example.
import pandas as pd
# Import the library or helper used in this example.
# Step 3 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.impute import SimpleImputer
# Create a small labelled dataset that is easy to inspect by eye.
# Step 4 — Construct `X` as a tabular object with named columns for inspectable analysis.
X = pd.DataFrame({"age":[24,np.nan,35,42,np.nan,51],"income":[45,52,np.nan,68,72,80]})
# Print this intermediate result so you can verify the workflow step by step.
# Step 5 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Original missing counts:", X.isna().sum().to_dict())
# Store this intermediate value with a descriptive name for the next step.
# Step 6 — Compute the right-hand expression and store its result in `imp` for the next step.
imp = SimpleImputer(strategy="median", add_indicator=True)
# Learn the transformation from this data and apply it in one step.
# Step 7 — Fit the transformation on the training input and immediately transform that same input.
Xt = imp.fit_transform(X)
# Print this intermediate result so you can verify the workflow step by step.
# Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · Output shape with indicators:", Xt.shape)
# Print this intermediate result so you can verify the workflow step by step.
# Step 9 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Transformed matrix:")
# Print this intermediate result so you can verify the workflow step by step.
# Step 10 — Display the current value explicitly so the result/state can be inspected during execution.
print(np.round(Xt, 1))STEP 1 · Original missing counts: {'age': 2, 'income': 1}
STEP 2 · Output shape with indicators: (6, 4)
STEP 3 · Transformed matrix:
[[24. 45. 0. 0. ]
[38.5 52. 1. 0. ]
[35. 68. 0. 1. ]
[42. 68. 0. 0. ]
[38.5 72. 1. 0. ]
[51. 80. 0. 0. ]]