[SPARK-7557] [ML] [DOC] User guide for spark.ml HashingTF, Tokenizer

Added feature transformer subsection to spark.ml guide, with HashingTF and Tokenizer.  Added JavaHashingTFSuite to test Java examples in new guide.

I've run Scala, Python examples in the Spark/PySpark shells.  I ran the Java examples via the test suite (with small modifications for printing).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #6093 from jkbradley/hashingtf-guide and squashes the following commits:

d5d213f [Joseph K. Bradley] small fix
dd6e91a [Joseph K. Bradley] fixes from code review of user guide
33c3ff9 [Joseph K. Bradley] small fix
bc6058c [Joseph K. Bradley] fix link
361a174 [Joseph K. Bradley] Added subsection for feature transformers to spark.ml guide, with HashingTF and Tokenizer.  Added JavaHashingTFSuite to test Java examples in new guide

1 files changed, 9 insertions, 0 deletions

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 771a07183e..b7b6376e06 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -148,6 +148,15 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s.
 For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
 This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
 
+# Algorithm Guides
+
+There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines.
+
+**Pipelines API Algorithm Guides**
+
+* [Feature Extraction, Transformation, and Selection](ml-features.html)
+
+
 # Code Examples
 
 This section gives code examples illustrating the functionality discussed above.