A minimum implementation might translate a NaN in input to a row of NaNs in output. I believe this would be the most consistent default behaviour with respect to other preprocessing tools, and with reasonable backwards-compatibility, but other core devs might disagree (see #10465 (comment)).

NaN should also be excluded from the categories identified in fit.

A handle_missing parameter might allow NaN in input to be:

• replaced with a row of NaNs as above
• replaced with a row of zeros
• represented with a separate one-hot column

in the output.

A missing_values parameter might allow the user to configure what object is a placeholder for missingness (e.g. NaN, None, etc.).

See #10465 for background