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attr ", "attr ", "attr " and "plan". you can modify the default datasource to read your custom properties or different entity type. in this example, we modify datasource to read properties "featurea", "featureb", "featurec", "featured" and "label" for entity type "item". you can find the complete modified

list of properties names in the required parameter modify how to create the labeledpoint object using the entity properties def readtraining(sc: sparkcontext): trainingdata = { ... val labeledpoints: rdd[labeledpoint] = peventstore.aggregateproperties( appname = dsp.appname, entitytype = "item", // modified...

http://predictionio.apache.org/templates/classification/reading-custom-properties/

attr ", "attr ", "attr " and "plan". you can modify the default datasource to read your custom properties or different entity type. in this example, we modify datasource to read properties "featurea", "featureb", "featurec", "featured" and "label" for entity type "item". you can find the complete modified

list of properties names in the required parameter modify how to create the labeledpoint object using the entity properties def readtraining(sc: sparkcontext): trainingdata = { ... val labeledpoints: rdd[labeledpoint] = peventstore.aggregateproperties( appname = dsp.appname, entitytype = "item", // modified...

https://predictionio.apache.org/templates/classification/reading-custom-properties/

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