varImp.RdVariable importance for neural networks in DeepGenomeScan.
## S3 method for class 'neuralnet' varImp(object, ...) ## S3 method for class 'h2o' varImp(object, ...) ## S3 method for class 'kears' varImp(object, ...) ## S3 method for class 'FCNN4R' varImp(object, ...) ## S3 method for class 'RSNNS' varImp(object, ...)
| object | The final fitted model or the model trained from DeepGenomeScan |
|---|---|
| ... | parameters to pass to the specific varImp methods |
The varible importance estimated here based on various methods. In terms of MLP from "neuralnet", "RSNNS", we used Olden et al (2003), which uses cas the sum of the product of raw input-hidden, hidden-output connection weights, proposed by Olden et al. 2004. In terms of the CNN and MLP importance extimated from Keras and Tensorflow libearies, we used themodel agnostic feature importance based on Ribeiro et al, 2018. Specifical CNN and MLP importance can see function CNN_varIMP_NULL_model, MLP_varIMP_NULL_model.
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Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016). Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. “Model Class Reliance: Variable importance measures for any machine learning model class, from the ‘Rashomon’ perspective.” http://arxiv.org/abs/1801.01489 (2018).
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