This Friday H2O will be at MLconf (http://mlconf.com) to give a live demo, introduce a customer use case, and talk about the implications of model specification in production. If you don’t get a chance to stop by our booth, or come see our demo, you can find the presentation slides on the MLconf website (they will be posted on Friday, April 11).
We’ll be walking through a practical example of different model choices and outcomes using the same predictors and target values. Generalized linear models have the benefit of interpretability and easy scaling of estimated parameters. When data are overly complex, GLM can easily be linked to PCA, or regularization can be applied to simplify the model. On the other hand, our Gradient Boosted Machine handles both classification and regression, takes a non-parametric approach, and quickly models even the most complex of interactions. Interpretability can be a bit harder, but identifying critical components is far easier with variable importance returned with the model output. You can also hear from our client, Collective, on their experiences with real world application in an H2O use case.