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Sean Owen commented on SPARK-1227: ---------------------------------- OK you're interested in detecting overfitting, for one? You can't compare algorithms with a different objective function this way though, right? a different regularization param alone means a different objective. So I don't see it can be a general benchmark, and other classifier metrics are more appropriate. But yeah you could compare two runs that share the exact same objective, and that's a way of comparing training algorithms. Accuracy is in MulticlassMetrics which can be used with binary classification but it would be a nice to have for BinaryClassificationMetrics. > Diagnostics for Classification&Regression > ----------------------------------------- > > Key: SPARK-1227 > URL: https://issues.apache.org/jira/browse/SPARK-1227 > Project: Spark > Issue Type: Improvement > Components: MLlib > Reporter: Martin Jaggi > Assignee: Martin Jaggi > > Currently, the attained objective function is not computed (for efficiency > reasons, as one evaluation requires one full pass through the data). > For diagnostics and comparing different algorithms, we should however provide > this as a separate function (one MR). > Doing this requires the loss and regularizer functions themselves, not only > their gradients (which are currently in the Gradient class). How about adding > the new function directly on the corresponding models in classification/* and > regression/* ? Any thoughts? -- 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