[jira] [Assigned] (SPARK-8298) Sliding Window CrossValidator

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-8298?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-8298:
-----------------------------------

    Assignee:     (was: Apache Spark)

> Sliding Window CrossValidator
> -----------------------------
>
>                 Key: SPARK-8298
>                 URL: https://issues.apache.org/jira/browse/SPARK-8298
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: Justin Yip
>
> CrossValidator only supports k-folds. It cannot prevent the validation data from look-ahead bias. I would like to contribute a sliding-window based CrossValidator. The sliding window guarantees a clear cutoff time between the training and validation data, to prevent look-ahead bias.
> Three parameters are used to govern the generation process.
> 1. numFold - Int
> 2. firstCutoffTime - Timestamp, the cutoff time of the training data for the first (training, validation) data pair
> 3. validationWindowSize - Long, millis of the validation data set duration.
> Need to decide:
> Whether to make the current CrossValidator more generic or implement a new SlidingWindowCrossValidator.
> - Most of the logic are identical between CrossValidator and SlidingWindowValidator, except for the part where the training-validation data pairs is generated. More, if we introduce other kinds of data splitting methods, there will be lots of code redundancy if we use multiple classes.
> - However, I also foresee that things will get messy to support too many splitting methods with one CrossValidator class.



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