# distinction between classify-samples and classify-samples-ncv **URL:** \ **Category:** User Support\ **Created:** [August 3, 2020, 4:08am UTC](https://forum.qiime2.org/t/distinction-between-classify-samples-and-classify-samples-ncv/15925 "2020-08-03T04:08:07Z")\ **Posts on this page:** 1\ **Showing post:** 4
**Author:** ![Nicholas\_Bokulich](https://forum.qiime2.org/user_avatar/forum.qiime2.org/nicholas_bokulich/32/19937_2.png) [@Nicholas\_Bokulich](https://forum.qiime2.org/u/Nicholas_Bokulich)\ **Post date:** [August 3, 2020, 1:53pm UTC](https://forum.qiime2.org/t/distinction-between-classify-samples-and-classify-samples-ncv/15925/4 "2020-08-03T13:53:50Z")
> [@devonorourke](#): > > When seeking to resolve which particy features are important, would the `ncv` method be preferable? In my opinion NCV is generally better, since it is training and testing across multiple iterations so gives a sense of variance in accuracy, as well as predictions for all samples. > [@devonorourke](#): > > Nevertheless, it seems like both `classify-samples` and `classify-samples-ncv` use a lot of the same terms Yes, because indeed these are using the same methods but different training/testing schemes. > [@devonorourke](#): > > one difference seems to be how many times a feature might be evaluated for it’s relative importance in training a model Not exactly — it's which samples are included in training, and how many times training occurs. This could indeed be a matter of which features are tested, but not necessarily. > [@devonorourke](#): > > With `classify-samples` , is the difference relative to NCV that your training set is _ **never** _ tested for importance (that is, just those in the _testing_ set are?)? No — importance is determined on the training set, so the difference is that importance is determined on only some of the samples and only once (whereas for NCV importance is averaged across each iteration). > [@devonorourke](#): > > Why would you use one over another? classify-samples is a complete pipeline and easier to use. It also outputs a trained classifier that can be re-used to predict other samples (NCV does not, since it trains K classifiers!). The motivations for NCV I mentioned above. > [@devonorourke](#): > > Can you use both to tease apart different aspects of your data? Both are perfectly valid to use, so it just depends on which is more suited to your use case... and the type of training/testing scheme you want to use. --- _[View the full topic](https://forum.qiime2.org/t/distinction-between-classify-samples-and-classify-samples-ncv/15925)._