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From Sean Owen <so...@cloudera.com>
Subject Re: Using SVMWithSGD model to predict
Date Sun, 19 Oct 2014 18:52:49 GMT
The problem is that you called clearThreshold(). The result becomes the SVM
margin not a 0/1 class prediction. There is no probability output.

There was a very similar question last week. Is there an example out there
suggesting clearThreshold()? I also wonder if it is good to overload the
meaning of the output indirectly this way.
On Oct 19, 2014 6:53 PM, "npomfret" <nick-nabble@snowmonkey.co.uk> wrote:

> Hi, I'm new to spark and just trying to make sense of the SVMWithSGD
> example. I ran my dataset through it and build a model. When I call
> predict() on the testing data (after clearThreshold()) I was expecting to
> get answers in the range of 0 to 1. But they aren't, all predictions seem
> to be negative numbers between -0 and -2. I guess my question is what do
> these predictions mean? How are they of use? The outcome I need is a
> probability rather than a binary. Here's my java code: SparkConf conf = new
> SparkConf() .setAppName("name") .set("spark.cores.max", "1");
> JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD points =
> sc.textFile(path).map(new ParsePoint()).cache(); JavaRDD training =
> points.sample(false, 0.8, 0L).cache(); JavaRDD testing =
> points.subtract(training); SVMModel model =
> SVMWithSGD.train(training.rdd(), 100); model.clearThreshold(); for
> (LabeledPoint point : testing.toArray()) { Double score =
> model.predict(point.features()); System.out.println("score = " +
> score);//<- all these are negative numbers, seemingly between 0 and -2 }
> ------------------------------
> View this message in context: Using SVMWithSGD model to predict
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