Build: Release notes for v1.7

Author: Rahul Iyer

ReleaseNotes.txt

@@ -8,6 +8,60 @@ A complete list of changes for each release can be obtained by viewing the git
 commit history located at https://github.com/madlib/madlib/commits/master.
 
 Current list of bugs and issues can be found at http://jira.madlib.net.
+--------------------------------------------------------------------------------
+MADlib v1.7
+
+Release Date: 2014-December-31
+
+New features:
+* Generalized Linear Model:
+    - Added a new generic module for GLM functions that allow for response
+    variables that have arbitrary distributions (rather than simply
+    Gaussian distributions), and for an arbitrary function of the response
+    variable (the link function) to vary linearly with the predicted values
+    (rather than assuming that the response itself must vary linearly).
+    - Available distribution families: gaussian (link functions: identity,
+    inverse and log), binomial (link functions: probit and logit),
+    poisson (link functions: log, identity and square-root), gamma (link
+    functions: inverse, identity and log) and inverse gaussian (link functions:
+    square-inverse, inverse, identity and log).
+    - Deprecated 'mlogregr_train' in favor of 'multinom' available as part of
+    the new GLM functionality.
+    - Added a new 'ordinal' function for ordered logit and probit regression.
+* Decision Tree: Reimplemented the decision tree module which includes following
+changes:
+    - Improved usability due to a new interface.
+    - Performance enhancements upto 40 times faster than the old interface.
+    - Additional features like pruning methods, surrogate variables for
+    NULL handling, cross validation, and various new tree tuning parameters.
+    - Addition of a new display function to visualize the trained tree and new
+    prediction function for scoring of new datasets.
+* Random Forest: Reimplemented the random forest module which includes following
+changes:
+    - New random forest module based on the new decision tree module.
+    - Better variable importance metrics and ability to explore each tree
+    in the forest independently.
+    - Ability to get class probabilities of all classes and not just the max
+    class during prediction.
+    - Improved visualization with export capabilities using Graphviz dot format.
+* PMML:
+    - Upgraded compatible PMML version to 4.1.
+    - Moved PMML export out of early stage development with new functionality
+    available to export GLM, decision tree, and random forest models.
+* Updated Eigen from 3.1.2 to 3.2.2.
+* Updated PyXB from 1.2.3 to 1.2.4.
+* Added finer granularity control for running specific install-check tests.
+
+Bug fixes:
+- Fixed bug in K-means allowing use of user-defined metric functions
+  (MADLIB-874, MADLIB-875).
+- Fixed issues related to header files included in the build system
+  (MADLIB-855, MADLIB-879, MADLIB-884).
+
+Known issues:
+- Performance for decision tree with cross-validation is poor on a HAWQ
+multi-node system.
+
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 MADlib v1.6