[jira] [Commented] (SPARK-9961) ML prediction abstractions should have defaultEvaluator fields
[ https://issues.apache.org/jira/browse/SPARK-9961?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14731838#comment-14731838 ] Joseph K. Bradley commented on SPARK-9961: -- By "evaluator," I mean the Evaluator types in spark.ml.evaluation, which can be used for model selection. > ML prediction abstractions should have defaultEvaluator fields > -- > > Key: SPARK-9961 > URL: https://issues.apache.org/jira/browse/SPARK-9961 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Joseph K. Bradley > > Predictor and PredictionModel should have abstract defaultEvaluator methods > which return Evaluators. Subclasses like Regressor, Classifier, etc. should > all provide natural evaluators, set to use the correct input columns and > metrics. Concrete classes may later be modified to > The initial implementation should be marked as DeveloperApi since we may need > to change the defaults later on. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9961) ML prediction abstractions should have defaultEvaluator fields
[ https://issues.apache.org/jira/browse/SPARK-9961?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14731810#comment-14731810 ] George Dittmar commented on SPARK-9961: --- Can you expand on what you mean by Evaluator? Just looking for something to eval how good predictions are? > ML prediction abstractions should have defaultEvaluator fields > -- > > Key: SPARK-9961 > URL: https://issues.apache.org/jira/browse/SPARK-9961 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Joseph K. Bradley > > Predictor and PredictionModel should have abstract defaultEvaluator methods > which return Evaluators. Subclasses like Regressor, Classifier, etc. should > all provide natural evaluators, set to use the correct input columns and > metrics. Concrete classes may later be modified to > The initial implementation should be marked as DeveloperApi since we may need > to change the defaults later on. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-9961) ML prediction abstractions should have defaultEvaluator fields
[ https://issues.apache.org/jira/browse/SPARK-9961?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14696640#comment-14696640 ] Apache Spark commented on SPARK-9961: - User 'mengxr' has created a pull request for this issue: https://github.com/apache/spark/pull/8190 > ML prediction abstractions should have defaultEvaluator fields > -- > > Key: SPARK-9961 > URL: https://issues.apache.org/jira/browse/SPARK-9961 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Joseph K. Bradley > > Predictor and PredictionModel should have abstract defaultEvaluator methods > which return Evaluators. Subclasses like Regressor, Classifier, etc. should > all provide natural evaluators, set to use the correct input columns and > metrics. Concrete classes may later be modified to > The initial implementation should be marked as DeveloperApi since we may need > to change the defaults later on. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
