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Old 05-10-2019, 10:43 AM
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I'm looking for feedback and recommendations from others who have used or trialed use of h2o for predictive modelling work. I'm curious what your experiences have been with it and if you have found any limitations or had struggles using it in lieu of alternative modelling platforms.

In particular, I am interested in using it as an alternative/supplement to tools like Emblem.
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Old 05-10-2019, 11:17 AM
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I like it. I've used both the R and Python versions, and appreciate the clean syntax and good documentation.
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Old 05-28-2019, 11:05 AM
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I've used it for Loss Cost modeling. Trying to build demand models with it, w/ less success.

I like it. Data wrangling can be a bit annoying, so I use tidy packages and get as much done as i can outside of h2o, before converting to an h2o dataframe.

The flow web interface is nice for keeping tabs on the progress of your models.

The biggest problem I've found is that there are few insurance related examples, but that can be said about ML in general. Seems like "data scientists" focus on classification, or imagine recognition. Highly skewed insurance loss cost data just doesn't seem all that prevalent in the literature.
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