It looks like you are directly computing the SVM decision function in
both cases:
val predictions2 = m_users_double.map{point=>
point.zip(weights).map(a=> a._1 * a._2).sum + intercept
}.cache()
clf.decision_function(T)
This does not give you +1/-1 in SVMs (well... not for most points,
which will be outside the margin around the separating hyperplane).
You can use the predict() function in SVMModel -- which will give you
0 or 1 (rather than +/- 1 but that's just differing convention)
depending on the sign of the decision function. I don't know if this
was in 0.9.
At the moment I assume you saw small values of the decision function
in scikit because of the radial basis function.
On Tue, Oct 7, 2014 at 7:45 PM, Sunny Khatri <sunny.k...@gmail.com> wrote:
> Not familiar with scikit SVM implementation ( and I assume you are using
> linearSVC). To figure out an optimal decision boundary based on the scores
> obtained, you can use an ROC curve varying your thresholds.
>
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