I normally work more on the effect estimation/causal inference side of things, where people are pretty comfortable with multiple imputation for missing data, but right now I'm working on a project that's more in the machine learning side of things.
We're expecting to have some missing data, because it's real world medical data, which invariably does.
The inclination of some collaborators is to go with the complete case type analysis, where only subjects with full data are used, but this makes me slightly nervous, as I feel like those missing data patterns might have an impact.
Is the "best practice" for machine learning tasks to use some form of imputation? If so, should this be done before feature selection?