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https://issues.apache.org/jira/browse/SPARK-61017?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ruifeng Zheng resolved SPARK-61017.
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Fix Version/s: 4.4.0
Resolution: Fixed
Issue resolved by pull request 60179
[https://github.com/apache/spark/pull/60179]
> Support configurable intermediate storage levels in iterative classifiers
> -------------------------------------------------------------------------
>
> Key: SPARK-61017
> URL: https://issues.apache.org/jira/browse/SPARK-61017
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Affects Versions: 5.0.0, 4.4.0
> Reporter: Ruifeng Zheng
> Assignee: Ruifeng Zheng
> Priority: Major
> Labels: pull-request-available
> Fix For: 4.4.0
>
>
> LogisticRegression, LinearSVC, FMClassifier, and
> MultilayerPerceptronClassifier currently persist intermediate training data
> at a hard-coded MEMORY_AND_DISK storage level. Caching the input DataFrame
> does not configure these internally created datasets.
> Expose the existing intermediateStorageLevel shared parameter on these four
> classifiers in Scala and PySpark. Preserve MEMORY_AND_DISK as the default,
> use the existing validation that rejects NONE and invalid storage levels, and
> pass the configured level to each algorithm's internal persistence call. For
> MultilayerPerceptronClassifier, forward it to FeedForwardTrainer.
> Cover parameter defaults, validation, copying and persistence, actual storage
> used during training, and PySpark parameter transfer.
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