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 as 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. This post would never happen if not for the inspiration I got from two excellent blog posts: Shirin Glander's Lime presentation and Matt Dancho's HR data analysis. There's no hiding that this post is basically copy-catting their work, at least I'm standing on the shoulders of giants, hey! I'll use the powerful h2o.automl() function to optimise and choose the most accurate model classifying benign and malignant cancer cells from the Wisconsin dataset. First, let's load the data: Now, let's set up the local H2O instance… Finally, we can now use the famous h2o.automl() function and set the model up: set the target, feature names, training and validation set, as well as how long we want the algorithm to run for (for this you can use either max_runtime_secs argument, like I did here, or max_models, see h2o.automl() documentation for details.