Χαίρε Αδαμάντιε Κοραή....έαν είναι πράγματι το όνομα σου..
Just to follow up on Liquan, you might be interested in removing the thresholds, and then treating the predictions as a probability from 0..1 inclusive. SVM with the linear kernel is a straightforward linear classifier -- so you with the model.clearThreshold() you can just get the raw predicted scores, removing the threshold which simple translates that into a positive/negative class. API is here http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.mllib.classification.SVMModel Enjoy! Aris On Sun, Sep 21, 2014 at 11:50 PM, Liquan Pei <liquan...@gmail.com> wrote: > HI Adamantios, > > For your first question, after you train the SVM, you get a model with a > vector of weights w and an intercept b, point x such that w.dot(x) + b = 1 > and w.dot(x) + b = -1 are points that on the decision boundary. The > quantity w.dot(x) + b for point x is a confidence measure of > classification. > > Code wise, suppose you trained your model via > val model = SVMWithSGD.train(...) > > and you can set a threshold by calling > > model.setThreshold(your threshold here) > > to set the threshold that separate positive predictions from negative > predictions. > > For more info, please take a look at > http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.classification.SVMModel > > For your second question, SVMWithSGD only supports binary classification. > > Hope this helps, > > Liquan > > On Sun, Sep 21, 2014 at 11:22 PM, Adamantios Corais < > adamantios.cor...@gmail.com> wrote: > >> Nobody? >> >> If that's not supported already, can please, at least, give me a few >> hints on how to implement it? >> >> Thanks! >> >> >> On Fri, Sep 19, 2014 at 7:43 PM, Adamantios Corais < >> adamantios.cor...@gmail.com> wrote: >> >>> Hi, >>> >>> I am working with the SVMWithSGD classification algorithm on Spark. It >>> works fine for me, however, I would like to recognize the instances that >>> are classified with a high confidence from those with a low one. How do we >>> define the threshold here? Ultimately, I want to keep only those for which >>> the algorithm is very *very* certain about its its decision! How to do >>> that? Is this feature supported already by any MLlib algorithm? What if I >>> had multiple categories? >>> >>> Any input is highly appreciated! >>> >> >> > > > -- > Liquan Pei > Department of Physics > University of Massachusetts Amherst >
