Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4861#discussion_r25658207
--- Diff: docs/mllib-linear-methods.md ---
@@ -144,41 +152,7 @@ denoted by $\x$, the model makes predictions based on
the value of $\wv^T \x$.
By the default, if $\wv^T \x \geq 0$ then the outcome is positive, and
negative
otherwise.
-### Logistic regression
-
-[Logistic regression](http://en.wikipedia.org/wiki/Logistic_regression) is
widely used to predict a
-binary response.
-It is a linear method as described above in equation
`$\eqref{eq:regPrimal}$`, with the loss
-function in the formulation given by the logistic loss:
-`\[
-L(\wv;\x,y) := \log(1+\exp( -y \wv^T \x)).
-\]`
-
-The logistic regression algorithm outputs a logistic regression model.
Given a
-new data point, denoted by $\x$, the model makes predictions by
-applying the logistic function
-`\[
-\mathrm{f}(z) = \frac{1}{1 + e^{-z}}
-\]`
-where $z = \wv^T \x$.
-By default, if $\mathrm{f}(\wv^T x) > 0.5$, the outcome is positive, or
-negative otherwise, though unlike linear SVMs, the raw output of the
logistic regression
-model, $\mathrm{f}(z)$, has a probabilistic interpretation (i.e., the
probability
-that $\x$ is positive).
-
-### Evaluation metrics
-
-MLlib supports common evaluation metrics for binary classification (not
available in PySpark).
-This
-includes precision, recall,
[F-measure](http://en.wikipedia.org/wiki/F1_score),
-[receiver operating characteristic
(ROC)](http://en.wikipedia.org/wiki/Receiver_operating_characteristic),
-precision-recall curve, and
-[area under the curves
(AUC)](http://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve).
-AUC is commonly used to compare the performance of various models while
-precision/recall/F-measure can help determine the appropriate threshold to
use
-for prediction purposes.
-
--- End diff --
I created a JIRA for evaluation metrics:
https://issues.apache.org/jira/browse/SPARK-6129. It is hard to find a position
for evaluation metrics in this guide.
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