  Cross-validation (CV) is a technique used to estimate generalization error
for prediction models. For pipeline modeling algorithms (i.e. modeling
procedures with multiple steps), it has been recommended the entire sequence of
steps be carried out during each replicate of CV to mimic the application of
the entire pipeline to an external testing set. While theoretically sound,
following this recommendation can lead to high computational costs when a
pipeline modeling algorithm includes computationally expensive operations, e.g.
imputation of missing values. There is a general belief that unsupervised
variable selection (i.e. ignoring the outcome) can be applied before conducting
CV without incurring bias, but there is less consensus for unsupervised
imputation of missing values. We empirically assessed whether conducting
unsupervised imputation prior to CV would result in biased estimates of
generalization error or result in poorly selected tuning parameters and thus
degrade the external performance of downstream models. Results show that
despite optimistic bias, the reduced variance of imputation before CV compared
to imputation during each replicate of CV leads to a lower overall root mean
squared error for estimation of the true external R-squared and the performance
of models tuned using CV with imputation before versus during each replication
is minimally different. In conclusion, unsupervised imputation before CV
appears valid in certain settings and may be a helpful strategy that enables
analysts to use more flexible imputation techniques without incurring high
computational costs.
