2012 IntelligibleModelsforClassifica

• (Lou et al., 2012) ⇒ Yin Lou, Rich Caruana, and Johannes Gehrke. (2012). “Intelligible Models for Classification and Regression.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339556

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Intelligible+Models+for+Classification+and+Regression
• http://dl.acm.org/citation.cfm?id=2339530.2339556&preflayout=flat#citedby

Quotes

Author Keywords

• Classification; induction; intelligible models; regression

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 IntelligibleModelsforClassifica	Johannes Gehrke Rich Caruana Yin Lou			Intelligible Models for Classification and Regression				10.1145/2339530.2339556		2012