Thanks it got solved ☺ From: Artem Aliev [mailto:artem.al...@gmail.com] Sent: Thursday, February 11, 2016 3:19 PM To: Sonal Goyal Cc: Chandan Verma; user@spark.apache.org Subject: Re: Getting prediction values in spark mllib
It depends on Algorithm you use NaiveBayesModel has predictProbabilities method to work dirrectly with probabilites the LogisiticRegresionModel and SVMModel clearThreshold() will make predict method returns probabilites as mentioned above On Thu, Feb 11, 2016 at 11:17 AM, Sonal Goyal <sonalgoy...@gmail.com<mailto:sonalgoy...@gmail.com>> wrote: Looks like you are doing binary classification and you are getting the label out. If you clear the model threshold, you should be able to get the raw score. On Feb 11, 2016 1:32 PM, "Chandan Verma" <chandan.ve...@citiustech.com<mailto:chandan.ve...@citiustech.com>> wrote: Following is the code Snippet JavaRDD<Tuple2<Object, Object>> predictionAndLabels = data .map(new Function<LabeledPoint, Tuple2<Object, Object>>() { public Tuple2<Object, Object> call(LabeledPoint p) { Double prediction = sameModel.predict(p.features()); return new Tuple2<Object, Object>(prediction, p.label()); } }); The line "sameModel.predict(p.features());" gives me the prediction as double value (eg 0.0 or 1.0) . How can i get the prediction value with more digits after decimal point.eg<http://point.eg> 0.23443333 etc =========================================================================================================================================================================================== DISCLAIMER: The information contained in this message (including any attachments) is confidential and may be privileged. If you have received it by mistake please notify the sender by return e-mail and permanently delete this message and any attachments from your system. Any dissemination, use, review, distribution, printing or copying of this message in whole or in part is strictly prohibited. Please note that e-mails are susceptible to change. CitiusTech shall not be liable for the improper or incomplete transmission of the information contained in this communication nor for any delay in its receipt or damage to your system. CitiusTech does not guarantee that the integrity of this communication has been maintained or that this communication is free of viruses, interceptions or interferences. ====================================================================================================================================================================
