I am trying to build a non-parametric model to predict pure premium of an insurance policy. Pure premium is simply the expected claim amount of an insurance policy.The problem is, most insurance policies do not result in claims and so the data set has a lot of zeroes in the response variable: over 90% of the observations made no claim. Existing methods to this prediction specifies a tweedie distribution for the response and uses GLM. But I do want to use non parametric methods to do this.
Is there anything that I can do to a response variable like this one since all the machine learning algorithms I have tried so far are giving me horrible results.