Name
Abstract | ||
|---|---|---|
Complex models for regression and classification have high accuracy, but are unfortunately no longer interpretable by users. We study the performance of generalized additive models (GAMs), which combine single-feature models called shape functions through a linear function. Since the shape functions can be arbitrarily complex, GAMs are more accurate than simple linear models. But since they do not contain any interactions between features, they can be easily interpreted by users. We present the first large-scale empirical comparison of existing methods for learning GAMs. Our study includes existing spline and tree-based methods for shape functions and penalized least squares, gradient boosting, and backfitting for learning GAMs. We also present a new method based on tree ensembles with an adaptive number of leaves that consistently outperforms previous work. We complement our experimental results with a bias-variance analysis that explains how different shape models influence the additive model. Our experiments show that shallow bagged trees with gradient boosting distinguish itself as the best method on low- to medium-dimensional datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2339530.2339556 | KDD |
Keywords | Field | DocType |
linear function,complex model,different shape model,intelligible model,shape function,generalized additive model,simple linear model,new method,additive model,tree-based method,best method,regression,linear model,classification | Least squares,Spline (mathematics),Data mining,Generalized additive model for location, scale and shape,Computer science,Artificial intelligence,Additive model,Pattern recognition,Regression,Linear model,Generalized additive model,Machine learning,Gradient boosting | Conference |
Citations | PageRank | References |
49 | 3.20 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Lou | 1 | 506 | 28.82 |
| Rich Caruana | 2 | 4503 | 655.71 |
| Johannes Gehrke | 3 | 13362 | 1055.06 |