On 03/12/2017 03:11 PM, Javier López Peña wrote:
The purpose is two-fold, on the one hand use the probabilities
generated by a very complex
model (e.g. a massive ensemble) to train a simpler one that achieves
comparable performance at a
fraction of the cost. Any universal classifier will do (neural
networks are the prime example).
You could use a regression model with a logistic sigmoid in the output
layer.
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