# FR: adding a 'fit stop' message to regressors

**URL:** <https://discourse.flucoma.org/t/fr-adding-a-fit-stop-message-to-regressors/2484>\
**Category:** New Ideas\
**Created:** [December 20, 2024, 7:11pm UTC](https://discourse.flucoma.org/t/fr-adding-a-fit-stop-message-to-regressors/2484 "2024-12-20T19:11:28Z")\
**Posts on this page:** 1\
**Showing post:** 7

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**Author:** ![rodrigo.constanzo](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/rodrigo.constanzo/32/12_2.png) [@rodrigo.constanzo](https://discourse.flucoma.org/u/rodrigo.constanzo)\
**Post date:** [December 21, 2024, 3:11pm UTC](https://discourse.flucoma.org/t/fr-adding-a-fit-stop-message-to-regressors/2484/7 "2024-12-21T15:11:03Z")

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> [@weefuzzy](#):
>
> As it stands, every time `fit` gets called a new partition is made, selecting a random portion for the training data and validation set. So, in that sense calling `fit` iteratively isn’t equivalent to calling it once with a big `maxIter`.

Riiiight. Ok, that wasn’t clear to me as I just assumed that some of that would persist over.

So the intended UX here is to just use a big chonky `maxiter` once and call it a day? (unless one is purposefully trying to cross-validate or do something special).

> [@weefuzzy](#):
>
> I know I’ve said it before, but a compiled partitioning object feels like an increasingly serious lack. Doing the partition at the point of dataset creation as you do here makes it less faffy than trying to partition extant data / label sets in extant code (which is variably dreadful in different environments: not even sure there would be a way in PD at the moment), but seems like it should be a one-click job in a better world.

Yes please!

For stuff in those other threads about [computing class means](https://discourse.flucoma.org/t/class-interpolation-distance-to-classes/1903), or [removing outliers](https://discourse.flucoma.org/t/removing-outliers-while-creating-classifiers/648), etc… it’s an absolute nightmare to code the manually separating/dumping/organizing of the sub-datasets, much more so than the actual computation being done (means/outliers).

> [@tedmoore](#):
>
> This patch could certainly be cleaned up maybe formed into a nice abstraction?

Interesting!

I’ve done this kind of stuff manually, which is a big pain in the ass. I do wonder how generalizable this would be using an arbitrary amount of classes etc… but being able to get a % correct out of the validation data would be super handy.

(I do wish the reported error was easier to tie to some kind of normalized range so it intrinsically communicated more)

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