Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4536#discussion_r24532966
 Diff: docs/mllibregression.md 
@@ 0,0 +1,161 @@
+
+layout: global
+title: Naive Bayes  MLlib
+displayTitle: <a href="mllibguide.html">MLlib</a>  Regression
+
+
+## Regression
+[Regression](http://en.wikipedia.org/wiki/Regression_analysis) is a statistical process
+for estimating the relationships among variables. It includes many techniques for modeling
+and analyzing several variables, when the focus is on the relationship between
+a dependent variable and one or more independent variables.
+
+## Isotonic regression
+[Isotonic regression](http://en.wikipedia.org/wiki/Isotonic_regression)
+belongs to the family of regression algorithms. Formally isotonic regression is a problem
where
+given a finite set of real numbers `$Y = {y_1, y_2, ..., y_n}$` representing observed
responses
+and `$X = {x_1, x_2, ..., x_n}$` the unknown response values to be fitted
+finding a function that minimises
+
+`\begin{equation}
+ f(x) = \sum_{i=1}^n w_i (y_i  x_i)^2
+\end{equation}`
+
+with respect to complete order subject to
+`$x_1\le x_2\le ...\le x_n$` where `$w_i$` are positive weights.
+The resulting function is called isotonic regression and it is unique.
+It can be viewed as least squares problem under order restriction.
+Essentially isotonic regression is a
+[monotonic function](http://en.wikipedia.org/wiki/Monotonic_function)
+best fitting the original data points.
+
+MLlib supports a
+[pool adjacent violators algorithm](http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf)
+which uses an approach to
+[parallelizing isotonic regression](http://softlib.rice.edu/pub/CRPCTRs/reports/CRPCTR96640.pdf).
 End diff 
Same here: http://doi.org/10.1007/9783642997891_10

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