Hi Justin, It sound like you're on the right track. The best way to write a custom Evaluator will probably be to modify an existing Evaluator as you described. It's best if you don't remove the other code, which handles parameter set/get and schema validation.
Joseph On Sun, May 17, 2015 at 10:35 PM, Justin Yip <yipjus...@prediction.io> wrote: > Hello, > > I would like to use other metrics in BinaryClassificaitonEvaluator, I am > thinking about simple ones (i.e. PrecisionByThreshold). From the api site, > I can't tell much about how to implement it. > > From the code, it seems like I will have to override this function, using > most of the existing code for checking column schema, then replace the line > which compute the actual score > <https://github.com/apache/spark/blob/1b8625f4258d6d1a049d0ba60e39e9757f5a568b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala#L72> > . > > Is my understanding correct? Or there are more convenient way of > implementing a metric in order to be used by ML pipeline? > > Thanks. > > Justin > > ------------------------------ > View this message in context: Implementing custom metrics under > MLPipeline's BinaryClassificationEvaluator > <http://apache-spark-user-list.1001560.n3.nabble.com/Implementing-custom-metrics-under-MLPipeline-s-BinaryClassificationEvaluator-tp22930.html> > Sent from the Apache Spark User List mailing list archive > <http://apache-spark-user-list.1001560.n3.nabble.com/> at Nabble.com. >
