[SPARK-15136][PYSPARK][DOC] Fix links to sphinx style and add a default param doc note

## What changes were proposed in this pull request?

PyDoc links in ml are in non-standard format. Switch to standard sphinx link format for better formatted documentation. Also add a note about default value in one place. Copy some extended docs from scala for GBT

## How was this patch tested?

Built docs locally.

Author: Holden Karau <holden@us.ibm.com>

Closes #12918 from holdenk/SPARK-15137-linkify-pyspark-ml-classification.

5 files changed, 40 insertions, 25 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index f032963334..c26c2d7fa5 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -353,7 +353,9 @@ class BinaryLogisticRegressionSummary(LogisticRegressionSummary):
         Returns the receiver operating characteristic (ROC) curve,
         which is an Dataframe having two fields (FPR, TPR) with
         (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
-        Reference: http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+
+        .. seealso:: `Wikipedia reference \
+        <http://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
 
         Note: This ignores instance weights (setting all to 1.0) from
         `LogisticRegression.weightCol`. This will change in later Spark
@@ -489,7 +491,7 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
                              TreeClassifierParams, HasCheckpointInterval, HasSeed, JavaMLWritable,
                              JavaMLReadable):
     """
-    `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
+    `Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_
     learning algorithm for classification.
     It supports both binary and multiclass labels, as well as both continuous and categorical
     features.
@@ -616,7 +618,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
                              RandomForestParams, TreeClassifierParams, HasCheckpointInterval,
                              JavaMLWritable, JavaMLReadable):
     """
-    `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
+    `Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_
     learning algorithm for classification.
     It supports both binary and multiclass labels, as well as both continuous and categorical
     features.
@@ -734,11 +736,21 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
                     GBTParams, HasCheckpointInterval, HasStepSize, HasSeed, JavaMLWritable,
                     JavaMLReadable):
     """
-    `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
+    `Gradient-Boosted Trees (GBTs) <http://en.wikipedia.org/wiki/Gradient_boosting>`_
     learning algorithm for classification.
     It supports binary labels, as well as both continuous and categorical features.
     Note: Multiclass labels are not currently supported.
 
+    The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.
+
+    Notes on Gradient Boosting vs. TreeBoost:
+    - This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
+    - Both algorithms learn tree ensembles by minimizing loss functions.
+    - TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes
+    based on the loss function, whereas the original gradient boosting method does not.
+    - We expect to implement TreeBoost in the future:
+    `SPARK-4240 <https://issues.apache.org/jira/browse/SPARK-4240>`_
+
     >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> from pyspark.ml.feature import StringIndexer
@@ -863,12 +875,12 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
                  HasRawPredictionCol, JavaMLWritable, JavaMLReadable):
     """
     Naive Bayes Classifiers.
-    It supports both Multinomial and Bernoulli NB. Multinomial NB
-    (`http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html`)
+    It supports both Multinomial and Bernoulli NB. `Multinomial NB
+    <http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html>`_
     can handle finitely supported discrete data. For example, by converting documents into
     TF-IDF vectors, it can be used for document classification. By making every vector a
-    binary (0/1) data, it can also be used as Bernoulli NB
-    (`http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html`).
+    binary (0/1) data, it can also be used as `Bernoulli NB
+    <http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html>`_.
     The input feature values must be nonnegative.
 
     >>> from pyspark.sql import Row
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index d2989fa4cd..606a6e7c22 100755
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -377,8 +377,8 @@ class DCT(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWrit
     The return vector is scaled such that the transform matrix is
     unitary (aka scaled DCT-II).
 
-    More information on
-    `https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia`.
+    .. seealso:: `More information on Wikipedia \
+    <https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia>`_.
 
     >>> from pyspark.mllib.linalg import Vectors
     >>> df1 = sqlContext.createDataFrame([(Vectors.dense([5.0, 8.0, 6.0]),)], ["vec"])
@@ -1108,8 +1108,8 @@ class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol, JavaMLRead
     """
     .. note:: Experimental
 
-    Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
-    which is available at `http://en.wikipedia.org/wiki/Polynomial_expansion`, "In mathematics, an
+    Perform feature expansion in a polynomial space. As said in `wikipedia of Polynomial Expansion
+    <http://en.wikipedia.org/wiki/Polynomial_expansion>`_, "In mathematics, an
     expansion of a product of sums expresses it as a sum of products by using the fact that
     multiplication distributes over addition". Take a 2-variable feature vector as an example:
     `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
@@ -2432,9 +2432,8 @@ class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol, JavaMLReadable, JavaM
 
     Implements the transforms required for fitting a dataset against an
     R model formula. Currently we support a limited subset of the R
-    operators, including '~', '.', ':', '+', and '-'. Also see the R formula
-    docs:
-    http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+    operators, including '~', '.', ':', '+', and '-'. Also see the `R formula docs
+    <http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html>`_.
 
     >>> df = sqlContext.createDataFrame([
     ...     (1.0, 1.0, "a"),
diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py
index db02684262..d7cb658465 100644
--- a/python/pyspark/ml/recommendation.py
+++ b/python/pyspark/ml/recommendation.py
@@ -54,8 +54,8 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     and update the products based on these messages.
 
     For implicit preference data, the algorithm used is based on
-    "Collaborative Filtering for Implicit Feedback Datasets", available
-    at `http://dx.doi.org/10.1109/ICDM.2008.22`, adapted for the blocked
+    `"Collaborative Filtering for Implicit Feedback Datasets",
+    <http://dx.doi.org/10.1109/ICDM.2008.22>`_, adapted for the blocked
     approach used here.
 
     Essentially instead of finding the low-rank approximations to the
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 04f566dfec..a2300fa49c 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -229,7 +229,9 @@ class LinearRegressionSummary(JavaWrapper):
         """
         Returns the explained variance regression score.
         explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
-        Reference: http://en.wikipedia.org/wiki/Explained_variation
+
+        .. seealso:: `Wikipedia explain variation \
+        <http://en.wikipedia.org/wiki/Explained_variation>`_
 
         Note: This ignores instance weights (setting all to 1.0) from
         `LinearRegression.weightCol`. This will change in later Spark
@@ -283,7 +285,9 @@ class LinearRegressionSummary(JavaWrapper):
     def r2(self):
         """
         Returns R^2^, the coefficient of determination.
-        Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination
+
+        .. seealso:: `Wikipedia coefficient of determination \
+        <http://en.wikipedia.org/wiki/Coefficient_of_determination>`
 
         Note: This ignores instance weights (setting all to 1.0) from
         `LinearRegression.weightCol`. This will change in later Spark
@@ -627,7 +631,7 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
                             DecisionTreeParams, TreeRegressorParams, HasCheckpointInterval,
                             HasSeed, JavaMLWritable, JavaMLReadable, HasVarianceCol):
     """
-    `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
+    `Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_
     learning algorithm for regression.
     It supports both continuous and categorical features.
 
@@ -782,7 +786,7 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
                             RandomForestParams, TreeRegressorParams, HasCheckpointInterval,
                             JavaMLWritable, JavaMLReadable):
     """
-    `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
+    `Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_
     learning algorithm for regression.
     It supports both continuous and categorical features.
 
@@ -890,7 +894,7 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                    GBTParams, HasCheckpointInterval, HasStepSize, HasSeed, JavaMLWritable,
                    JavaMLReadable):
     """
-    `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
+    `Gradient-Boosted Trees (GBTs) <http://en.wikipedia.org/wiki/Gradient_boosting>`_
     learning algorithm for regression.
     It supports both continuous and categorical features.
 
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 8978f028c5..411e377a56 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -2274,9 +2274,9 @@ class RDD(object):
         Return approximate number of distinct elements in the RDD.
 
         The algorithm used is based on streamlib's implementation of
-        "HyperLogLog in Practice: Algorithmic Engineering of a State
-        of The Art Cardinality Estimation Algorithm", available
-        <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+        `"HyperLogLog in Practice: Algorithmic Engineering of a State
+        of The Art Cardinality Estimation Algorithm", available here
+        <http://dx.doi.org/10.1145/2452376.2452456>`_.
 
         :param relativeSD: Relative accuracy. Smaller values create
                            counters that require more space.