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"Comparing Variable Importance from Ensemble and Deep Learning Methods for AdTech Data"
Variable Importance brings interpretability to popular black box modeling techniques. In this talk we study performance of popular ensemble techniques like Random Forest, Gradient Boosting with GLM. We observe certain traits that get magnified by non-linear techniques like Deep Learning that are otherwise missed by GBM or Random Forest.
We describe Open Source Scalable Machine Learning package, H2O which through ease-of-use and speed makes comparisons and picking best-of-breed and ensembles more natural. H2O's implementation of these algorithms tracks popular open source and text book implementations closely.