Reluctant Generalised Additive Modelling.
Reluctant Generalised Additive Modelling.
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DOI：
10.1111/insr.12429
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发表时间：
2020-12
影响因子：
2
通讯作者：
Tibshirani, Robert
中科院分区：
文献类型：
--
作者：
Tay, J. Kenneth;Tibshirani, Robert
关键词：
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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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
EFRON, B
DOI：
10.1080/10618600.2015.1089775
发表时间：
2016-12-01
影响因子：
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