From 719d0098bb08b50e523cec3e388115d5a206512b Mon Sep 17 00:00:00 2001 From: Manish Amde Date: Wed, 23 Apr 2014 17:04:05 -0700 Subject: [PATCH] updating user documentation --- docs/mllib-classification-regression.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/docs/mllib-classification-regression.md b/docs/mllib-classification-regression.md index 2c42f60c2ecce..b06e799577fb4 100644 --- a/docs/mllib-classification-regression.md +++ b/docs/mllib-classification-regression.md @@ -294,12 +294,9 @@ The recursive tree construction is stopped at a node when one of the two conditi 1. The node depth is equal to the `maxDepth` training paramemter 2. No split candidate leads to an information gain at the node. -### Practical Limitations - -The tree implementation stores an Array[Double] of size *O(#features \* #splits \* 2^maxDepth)* in memory for aggregating histograms over partitions. The current implementation might not scale to very deep trees since the memory requirement grows exponentially with tree depth. - -Please drop us a line if you encounter any issues. We are planning to solve this problem in the near future and real-world examples will be great. +### Implementation Details +The tree implementation stores an Array[Double] of size *O(#features \* #splits \* 2^maxDepth)* in memory for aggregating histograms over partitions. Based upon the 'maxMemory' parameter set during training (default is 128 MB), the task is broken down into smaller groups to avoid out-of-memory errors during computation. ## Implementation in MLlib