From 07d72fe6965aaf299d61bf6156d48bcfebc41b32 Mon Sep 17 00:00:00 2001 From: Manish Amde Date: Tue, 15 Apr 2014 11:14:28 -0700 Subject: Decision Tree documentation for MLlib programming guide Added documentation for user to use the decision tree algorithms for classification and regression in Spark 1.0 release. Apart from a general review, I need specific input on the following: * I had to move a lot of the existing documentation under the *linear methods* umbrella to accommodate decision trees. I wonder if there is a better way to organize the programming guide given we are so close to the release. * I have not looked closely at pyspark but I am wondering new mllib algorithms are automatically plugged in or do we need to some extra work to call mllib functions from pyspark. I will add to the pyspark examples based upon the advice I get. cc: @mengxr, @hirakendu, @etrain, @atalwalkar Author: Manish Amde Closes #402 from manishamde/tree_doc and squashes the following commits: 022485a [Manish Amde] more documentation 865826e [Manish Amde] minor: grammar dbb0e5e [Manish Amde] minor improvements to text b9ef6c4 [Manish Amde] basic decision tree code examples 6e297d7 [Manish Amde] added subsections f427e84 [Manish Amde] renaming sections 9c0c4be [Manish Amde] split candidate 6925275 [Manish Amde] impurity and information gain 94fd2f9 [Manish Amde] more reorg b93125c [Manish Amde] more subsection reorg 3ecb2ad [Manish Amde] minor text addition 1537dd3 [Manish Amde] added placeholders and some doc d06511d [Manish Amde] basic skeleton --- docs/mllib-guide.md | 1 + 1 file changed, 1 insertion(+) (limited to 'docs/mllib-guide.md') diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index eff856104c..1ac5cc13db 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -21,6 +21,7 @@ The following links provide a detailed explanation of the methods and usage exam * Least Squares * Lasso * Ridge Regression + * Decision Tree (for classification and regression) * Clustering * k-Means * Collaborative Filtering -- cgit v1.2.3