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Assume that a classier has been trained already (no missing training data), but a prediction has been requested based on an observation that does not include every feature. How can we handle this missing feature?

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You tend to avoid these situations while preprocessing your data. You impute the missing data. In production terms, frameworks like H2O handle quite elegantly. If you mean that there's a dimension mismatch, then H2O can still handle it.

H2O Missing values

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