[
https://issues.apache.org/jira/browse/SPARK-16206?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15361737#comment-15361737
]
Rémi Delassus commented on SPARK-16206:
---------------------------------------
>You can implement whatever you want to produce folds though, so not clear what
>this is about.
I don't think so?
The `fit` method explictly calls the Kfold method, and there is no way to tell
it to use another one.
How would you do so?
> Defining our own folds using CrossValidator
> -------------------------------------------
>
> Key: SPARK-16206
> URL: https://issues.apache.org/jira/browse/SPARK-16206
> Project: Spark
> Issue Type: Wish
> Components: ML
> Affects Versions: 1.6.2
> Reporter: Danilo Bustos
> Priority: Trivial
>
> I have been using cross validation process in order to train a Naive Bayes
> Model and I realize that it uses kFold method to get the random sampling data
> in order to create the folds. This method return an Array[(RDD[T], RDD[T])]
> of tuples, which I think are the set of different combination of the folds
> for training and testing.
> My question is whether there is any specific reason because the API does not
> allow you to define your own array of folds. I think would be a good idea if
> this capability is supported, it would help a lot.
> Please refer to:
> http://stackoverflow.com/questions/37868984/why-we-can-not-define-our-own-folds-when-we-are-using-crossvalidator
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
