Automated and Unmysterious Machine Learning in Cancer Detection
I get bored from doing two things: i) spot-checking + optimising parameters of my predictive models and ii) reading about how ‘black box’ machine learning (particularly deep learning) models are and how little we can do to better understand how they learn (or not learn, for example when they take a panda bear for a vulture!). In this post I’ll test a) H2O’s function h2o.automl() that may help me automate the former and b) Thomas Lin Pedersen’s library(lime) that may help clarify the latter.