Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3461#discussion_r21114104
--- Diff: docs/mllib-gbt.md ---
@@ -0,0 +1,308 @@
+---
+layout: global
+title: Gradient-Boosted Trees - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Gradient-Boosted Trees
+---
+
+* Table of contents
+{:toc}
+
+[Gradient-Boosted Trees
(GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting)
+are ensembles of [decision trees](mllib-decision-tree.html).
+GBTs iteratively train decision trees in order to minimize a loss function.
+Like decision trees, GBTs handle categorical features,
+extend to the multiclass classification setting, do not require
+feature scaling, and are able to capture non-linearities and feature
interactions.
+
+MLlib supports GBTs for binary classification and for regression,
+using both continuous and categorical features.
+MLlib implements GBTs using the existing [decision
tree](mllib-decision-tree.html) implementation. Please see the decision tree
guide for more information on trees.
+
+*Note*: GBTs do not yet support multiclass classification. For multiclass
problems, please use
+[decision trees](mllib-decision-tree.html) or [Random
Forests](mllib-random-forest.html).
+
+## Basic algorithm
+
+Gradient boosting iteratively trains a sequence of decision trees.
+On each iteration, the algorithm uses the current ensemble to predict the
label of each training instance and then compares the prediction with the true
label. The dataset is re-labeled to put more weight on training instances with
poor predictions. Thus, in the next iteration, the decision tree will help
correct for previous mistakes.
+
+The specific weight mechanism is defined by a loss function (discussed
below). With each iteration, GBTs further reduce this loss function on the
training data.
+
+### Comparison with Random Forests
--- End diff --
Should we have a new Ensembles section in the guide? It might be pretty
short. (And I haven't seen experimental results in the guide; would they
belong elsewhere?)
Eventually, I could imagine either (a) an Ensembles section once we have
more ensemble algs or (b) a section in the guide covering all algorithms and
how to choose between them.
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