One can set the parameter “stopping_metric” to “deviance” for the deeplearning algorithm (http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2o.estimators.deeplearning.H2ODeepLearningEstimator.stopping_metric).
In the FAQs (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/deep-learning.html), the answer to the question “How is deviance computed for a Deep Learning regression model?” is given by the following formula:
“Loss = Quadratic -> MSE==Deviance For Absolute/Laplace or Huber -> MSE != Deviance.”
As far as I understand, the deviance equals the MSE if the loss function is quadratic. How is it defined in the other cases (e.g. Huber)? I read the definition that in this case the deviance is not the MSE but what is it then?
Looking forward to your answer. Thanks in advance.