Scalable Look-Ahead Linear Regression Trees

author: David Vogel, AI Insight, University of Central Florida
published: Aug. 15, 2007,   recorded: August 2007,   views: 3376

See Also:

Download slides icon Download slides: LLRTDavid Vogel.ppt (316.0┬áKB)


Help icon Streaming Video Help

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

The motivation behind Look-ahead Linear Regression Trees (LLRT) is that out of all the methods proposed to date, there has been no scalable approach to exhaustively evaluate all possible models in the leaf nodes in order to obtain an optimal split. Using several optimizations, LLRT is able to generate and evaluate thousands of linear regression models per second. This allows for a near-exhaustive evaluation of all possible splits in a node, based on the quality of fit of linear regression models in the resulting branches. We decompose the calculation of the Residual Sum of Squares in such a way that a large part of it is pre-computed. The resulting method is highly scalable. We observe it to obtain high predictive accuracy for problems with strong mutual dependencies between attributes. We report on experiments with two simulated and seven real data sets.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: