Plane answers to complex questions: the theory of linear models
Plane answers to complex questions: the theory of linear models
Inducing Models of human Control Skills
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Discovering additive structure in black box functions
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Mining citizen science data to predict orevalence of wild bird species
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate intelligible models with pairwise interactions
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be expressed as the sum of lower-dimensional components. When variables interact, however, their effects are not additive and must be modeled and interpreted simultaneously. We present a new approach for the problem of interaction detection. Our method is based on comparing the performance of unrestricted and restricted prediction models, where restricted models are prevented from modeling an interaction in question. We show that an additive model-based regression ensemble, Additive Groves, can be restricted appropriately for use with this framework, and thus has the right properties for accurately detecting variable interactions.