Dear KNIME people,
Cross validation metanode is a great tool both for model assessment and model selection purposes. All the options it provides (e.g. linear, random & stratified sampling) are really well designed for its purpose. The only downside I noticed is that it currently does not run in parallel. This causes that the cross validation execution of most computationally intensive learner models is quite slow.
As the cross validation procedure mainly deals with independent model learner and prediction activities it would be simply great to have this parallelization (manually settable or automatically dependent on system processor count) implemented inside the cross validation nodes. Do you think it would be possible?