Fix feature-count and feature-order mismatches during prediction

A deployed model expects six features but receives five, or receives the same fields in a different order. The first case may fail loudly; the second can produce plausible but wrong predictions.

Why this happens

Training and serving disagree about the input schema. Manual array construction is especially vulnerable when fields are added, renamed or rearranged.

Step-by-step solution

1. Persist the expected field names, types and units with the model. Validate incoming records against that contract.

2. Construct the feature table in the explicit training order, or use named-column preprocessing that selects the intended fields.

3. Reject missing required fields and unexpected schema versions with a clear error. Do not pad absent features with arbitrary zeros.

Worked example

expected = ['visits', 'days_active', 'orders']
record = {'orders':2, 'visits':10, 'days_active':5}
row = [record[name] for name in expected]
assert row == [10,5,2]

Verify the fix

Shuffle the JSON key order and confirm predictions do not change. Remove a required field and confirm validation fails before the estimator is called.

Prevent a repeat

Version schema and model together and run the same fixture through training export and serving code. Keep feature engineering out of duplicated ad hoc scripts.

Reference

Official documentation or standards guidance

An original Gistmax troubleshooting guide. Scenarios and sample values are illustrative; configuration depends on your software version and environment. Cover diagram: Gistmax Tutorials.