Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7655#discussion_r35720926
--- Diff: docs/mllib-evaluation-metrics.md ---
@@ -0,0 +1,1476 @@
+---
+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 these algorithms are applied to build machine learning
models, there is a need to evaluate the performance
+of the model on some criteria, which depends on the application and its
requirements. Spark's MLlib also provides a
+suite of metrics for the purpose of evaluating the performance of machine
learning models.
+
+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.
+
+## Classification Model Evaluation
+
+While there are many different types of classification algorithms, the
evaluation of classification models all share
+similar principles. In a [supervised classification
problem](https://en.wikipedia.org/wiki/Statistical_classification),
+there exists a true output and a model-generated predicted output for each
data point. For this reason, the results for
+each data point can be assigned to one of four categories:
+
+* True Positive (TP) - class predicted by model and class in true output
--- End diff --
This is confusing to me: `class predicted by model and class in true
output`. I think the main issue is the term `true output`, which is not used in
MLlib doc before. The following is easier to understand (at least to me):
* TP: label is positive and prediction is also positive
* TN: label is negative and prediction is also negative
* FP: label is negative but prediction is positive
* FN: label is positive but prediction is negative
For multiclass problems, we can define what `positive` and `negative` means
under the context of a particular class.
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