research-article

Intelligible models for classification and regression

Authors:

Johannes GehrkeAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 150 - 158

https://doi.org/10.1145/2339530.2339556

Published: 12 August 2012 Publication History

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.

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Index Terms

Intelligible models for classification and regression
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning

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cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 ACM.

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Published: 12 August 2012

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.58496/ADSA/2024/006
Servati MVaccaro CDiller EPellegrino Da Silva RMafra FCao SStanley KCohen-Gadol AParker J(2024)Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep LearningMetabolites10.3390/metabo1406033714:6(337)Online publication date: 16-Jun-2024
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Hodgman MMinoccheri CMathis MWittrup ENajarian K(2024)A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial InfarctionDiagnostics10.3390/diagnostics1416174114:16(1741)Online publication date: 10-Aug-2024
https://doi.org/10.3390/diagnostics14161741
Sun YChen CXu YXie SBlum RVenkitasubramaniam P(2024)On the generalization discrepancy of spatiotemporal dynamics-informed graph convolutional networksFrontiers in Mechanical Engineering10.3389/fmech.2024.139713110Online publication date: 12-Jul-2024
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