Handle Missing Data in ML Models

EasyMachine Learning00:00

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Problem

You are training a supervised learning model for a production dataset with missing values in several columns. Some fields are sparse, and some may be missing for reasons that are tied to the label.

Question

How would you handle missing data in a dataset?

What matters

Differentiate random missingness from informative missingness.
Decide when to impute, add missing indicators, or drop a feature.
Keep preprocessing consistent between training and inference.