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https://issues.apache.org/jira/browse/SPARK-19053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16850527#comment-16850527
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zhengruifeng commented on SPARK-19053:
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Just remember this ticket.
Currently I find another method to 'support' multi-metric evaluation, in
[SPARK-27867|https://issues.apache.org/jira/browse/SPARK-27867]
the idea is to cache the lastest inputs and interenal mllib.metrics, and then
if the next evaluate call keep the inputs (except the metricName), then we can
directly obtain the metric from the internal intermediate summary, without any
computation.
[~josephkb] [~yuhaoyan] [~imatiach] how do you think about this?
> 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
> Priority: Major
>
> 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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