Mailing-List: contact issues-help@flink.apache.org; run by ezmlm
Precedence: bulk
Reply-To: dev@flink.apache.org
Date: Mon, 22 Jun 2015 13:25:00 +0000 (UTC)
From: "Mikio Braun (JIRA)" <jira@apache.org>
To: issues@flink.apache.org
Message-ID: <JIRA.12835335.1433409019000.135456.1434979500867@Atlassian.JIRA>
In-Reply-To: <JIRA.12835335.1433409019000@Atlassian.JIRA>
References: <JIRA.12835335.1433409019000@Atlassian.JIRA>
 <JIRA.12835335.1433409019470@arcas>
Subject: [jira] [Assigned] (FLINK-2157) Create evaluation framework for ML
 library
MIME-Version: 1.0
Content-Type: text/plain; charset=utf-8
Content-Transfer-Encoding: 7bit


     [ https://issues.apache.org/jira/browse/FLINK-2157?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mikio Braun reassigned FLINK-2157:
----------------------------------

    Assignee:     (was: Mikio Braun)

> Create evaluation framework for ML library
> ------------------------------------------
>
>                 Key: FLINK-2157
>                 URL: https://issues.apache.org/jira/browse/FLINK-2157
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>              Labels: ML
>
> Currently, FlinkML lacks means to evaluate the performance of trained models. It would be great to add some {{Evaluators}} which can calculate some score based on the information about true and predicted labels. This could also be used for the cross validation to choose the right hyper parameters.
> Possible scores could be F score [1], zero-one-loss score, etc.
> Resources
> [1] [http://en.wikipedia.org/wiki/F1_score]


--
This message was sent by Atlassian JIRA
(v6.3.4#6332)