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https://issues.apache.org/jira/browse/SPARK-19053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16297887#comment-16297887
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yuhao yang commented on SPARK-19053:
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Plan for further development:
1. Initial API and function parity with ML Evaluators. (This PR)
2. Python API.
3. Function parity with MLlib Metrics.
4. Add requested enhancements like including weight, add per-row metrics, add
ranking metrics.
5. Reorganize classification Metrics hierarchy, so Binary Classification
Metrics can support metrics in MultiClassMetrics (accuracy, recall etc.).
6. Possibly to be used in training summary.
> Supporting multiple evaluation metrics in DataFrame-based API: discussion
> -------------------------------------------------------------------------
>
> Key: SPARK-19053
> URL: https://issues.apache.org/jira/browse/SPARK-19053
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: Joseph K. Bradley
>
> This JIRA is to discuss supporting the computation of multiple evaluation
> metrics efficiently in the DataFrame-based API for MLlib.
> In the RDD-based API, RegressionMetrics and other *Metrics classes support
> efficient computation of multiple metrics.
> In the DataFrame-based API, there are a few options:
> * model/result summaries (e.g., LogisticRegressionSummary): These currently
> provide the desired functionality, but they require a model and do not let
> users compute metrics manually from DataFrames of predictions and true labels.
> * Evaluator classes (e.g., RegressionEvaluator): These only support computing
> a single metric in one pass over the data, but they do not require a model.
> * new class analogous to Metrics: We could introduce a class analogous to
> Metrics. Model/result summaries could use this internally as a replacement
> for spark.mllib Metrics classes, or they could (maybe) inherit from these
> classes.
> Thoughts?
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