[SPARK-15608][ML][EXAMPLES][DOC] add examples and documents of ml.isotonic regression

## What changes were proposed in this pull request?

add ml doc for ml isotonic regression
add scala example for ml isotonic regression
add java example for ml isotonic regression
add python example for ml isotonic regression

modify scala example for mllib isotonic regression
modify java example for mllib isotonic regression
modify python example for mllib isotonic regression

add data/mllib/sample_isotonic_regression_libsvm_data.txt
delete data/mllib/sample_isotonic_regression_data.txt
## How was this patch tested?

N/A

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #13381 from WeichenXu123/add_isotonic_regression_doc.

1 files changed, 70 insertions, 0 deletions

diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md
index d7e5521cbc..3d6106b532 100644
--- a/docs/ml-classification-regression.md
+++ b/docs/ml-classification-regression.md
@@ -691,6 +691,76 @@ The implementation matches the result from R's survival function
 </div>
 
 
+## 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.
+
+We implement a
+[pool adjacent violators algorithm](http://doi.org/10.1198/TECH.2010.10111)
+which uses an approach to
+[parallelizing isotonic regression](http://doi.org/10.1007/978-3-642-99789-1_10).
+The training input is a DataFrame which contains three columns
+label, features and weight. Additionally IsotonicRegression algorithm has one
+optional parameter called $isotonic$ defaulting to true.
+This argument specifies if the isotonic regression is
+isotonic (monotonically increasing) or antitonic (monotonically decreasing).
+
+Training returns an IsotonicRegressionModel that can be used to predict
+labels for both known and unknown features. The result of isotonic regression
+is treated as piecewise linear function. The rules for prediction therefore are:
+
+* If the prediction input exactly matches a training feature
+  then associated prediction is returned. In case there are multiple predictions with the same
+  feature then one of them is returned. Which one is undefined
+  (same as java.util.Arrays.binarySearch).
+* If the prediction input is lower or higher than all training features
+  then prediction with lowest or highest feature is returned respectively.
+  In case there are multiple predictions with the same feature
+  then the lowest or highest is returned respectively.
+* If the prediction input falls between two training features then prediction is treated
+  as piecewise linear function and interpolated value is calculated from the
+  predictions of the two closest features. In case there are multiple values
+  with the same feature then the same rules as in previous point are used.
+
+### Examples
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [`IsotonicRegression` Scala docs](api/scala/index.html#org.apache.spark.ml.regression.IsotonicRegression) for details on the API.
+
+{% include_example scala/org/apache/spark/examples/ml/IsotonicRegressionExample.scala %}
+</div>
+<div data-lang="java" markdown="1">
+
+Refer to the [`IsotonicRegression` Java docs](api/java/org/apache/spark/ml/regression/IsotonicRegression.html) for details on the API.
+
+{% include_example java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.java %}
+</div>
+<div data-lang="python" markdown="1">
+
+Refer to the [`IsotonicRegression` Python docs](api/python/pyspark.ml.html#pyspark.ml.regression.IsotonicRegression) for more details on the API.
+
+{% include_example python/ml/isotonic_regression_example.py %}
+</div>
+</div>
+
+
 
 # Decision trees