Reluctant Generalised Additive Modelling |
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| Authors: | J Kenneth Tay Robert Tibshirani |
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| Institution: | 1. Department of Statistics, Stanford University, Stanford, California, USA;2. Department of Statistics, Stanford University, Stanford, California, USA
Department of Biomedical Data Science, Stanford University, Stanford, California, USA |
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| Abstract: | Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples. |
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| Keywords: | Feature selection generalised additive models high-dimensional non-linear regression sparsity |
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