Github user atalwalkar commented on a diff in the pull request:
https://github.com/apache/spark/pull/1908#discussion_r16149793
--- Diff: docs/mllib-linear-methods.md ---
@@ -134,39 +131,42 @@ By default, linear SVMs are trained with an L2
regularization.
We also support alternative L1 regularization. In this case,
the problem becomes a [linear
program](http://en.wikipedia.org/wiki/Linear_programming).
-Linear SVM algorithm outputs a SVM model, which makes predictions based on
the value of $\wv^T \x$.
-By the default, if $\wv^T \x \geq 0$, the outcome is positive, or negative
otherwise.
-However, quite often in practice, the default threshold $0$ is not a good
choice.
-The threshold should be determined via model evaluation.
+The linear SVMs algorithm outputs an SVMs model. Given a new data point,
denoted by $\x$, the model makes predictions based on the value of $\wv^T \x$.
--- End diff --
fixed.
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