Name
Abstract | ||
|---|---|---|
Most decision tree algorithms base their splitting decisions on a piecewise constant model. Often these splitting algorithms are extrapolated to trees with non-constant models at the leaf nodes. 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1281192.1281273 | KDD |
Keywords | Field | DocType |
scalable approach,look-ahead linear regression trees,possible model,resulting method,linear regression model,scalable look-ahead linear regression,residual sum,splitting algorithm,possible split,leaf node,splitting decision,decision tree,look ahead,linear regression,predictive model,regression,sum of squares | Data mining,Multivariate adaptive regression splines,Principal component regression,Computer science,Linear model,Polynomial regression,Proper linear model,Artificial intelligence,Log-linear model,Linear predictor function,Machine learning,Linear regression | Conference |
Citations | PageRank | References |
10 | 0.62 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David S. Vogel | 1 | 40 | 4.49 |
| Ognian Asparouhov | 2 | 24 | 2.12 |
| Tobias Scheffer | 3 | 1862 | 139.64 |