Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/7655#discussion_r35480987
--- Diff: docs/mllib-metrics.md ---
@@ -0,0 +1,1464 @@
+---
+layout: global
+title: Evaluation Metrics - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Evaluation Metrics
+---
+
+* Table of contents
+{:toc}
+
+
+## Algorithm Metrics
+
+Spark's MLlib comes with a number of machine learning algorithms that can
be used to learn from and make predictions
+on data. When applying these algorithms, there is a need to evaluate their
performance on certain criteria, depending
+on the application and its requirements. Spark's MLlib also provides a
suite of metrics for the purpose of evaluating the
+performance of its algorithms.
+
+Specific machine learning algorithms fall under broader types of machine
learning applications like classification,
+regression, clustering, etc. Each of these types have well established
metrics for performance evaluation and those
+metrics that are currently available in Spark's MLlib are detailed in this
section.
+
+## Binary Classification
+
+[Binary classifiers](https://en.wikipedia.org/wiki/Binary_classification)
are used to separate the elements of a given
+dataset into one of two possible groups (e.g. fraud or not fraud) and is a
special case of multiclass classification.
+Most binary classification metrics can be generalized to multiclass
classification metrics.
+
+<table class="table">
+ <thead>
+ <tr><th>Metric</th><th>Definition</th></tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>Precision (Postive Predictive Value)</td>
--- End diff --
Terms like TP aren't explained and won't be obvious to the reader that
turns to this document, I think, for an explanation. The mathematical
definition of things like ROC is OK but this offers no intuition about them. I
don't think we need to reproduce a text on what they mean, but at least a
hyperlink to wikipedia?
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