Does it make sense to check for and remove linear combinations in the expanded matrix? Scenarios such as the coincidence of missing values should probably be condensed to a single column. In some cases I don't think it makes a difference but if the modeling algorithm relies on non-singularity or incorporates randomization of covariates, it should have an impact.
Does it make sense to check for and remove linear combinations in the expanded matrix? Scenarios such as the coincidence of missing values should probably be condensed to a single column. In some cases I don't think it makes a difference but if the modeling algorithm relies on non-singularity or incorporates randomization of covariates, it should have an impact.