Handle Missing Values in Classification

EasyMachine Learning00:00

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Problem

You are working on a supervised classification problem and several input fields have missing values. The team needs a modeling approach that handles the gaps without hurting predictive performance.

Question

How would you handle missing values in a classification dataset?

Data

rows·120Ktarget·Binary response labelfeatures·38missingness·2% to 35% by columnfeature_types·Numerical, categorical, timestamp-derived