You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/12/20 04:57:00 UTC
[jira] [Assigned] (SPARK-19053) Supporting multiple evaluation
metrics in DataFrame-based API: discussion
[ https://issues.apache.org/jira/browse/SPARK-19053?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-19053:
------------------------------------
Assignee: (was: Apache Spark)
> 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?
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org