So bringing them together will make for an awesome combination! Let's work out how to deal with this. To follow this post you'll need the following packages: Our example will be to try and predict whether tumours are cancerous or not using the Breast Cancer Wisconsin (Diagnostic) Data Set. For this example, we'll use pipelearner to perform a grid search of some xgboost hyperparameters. Grid searching is easy with pipelearner.

This post will demonstrate some examples of what pipeleaner can currently do. Fitting all of these models takes about four lines of code in pipelearner. Head to the pipelearner Github page to learn more and contact me if you have a chance to test it yourself or are interested in contributing (my contact details are at the end of this post). Thanks for reading and I hope this was useful for you. For updates of recent blog posts, follow @drsimonj on Twitter, or email me at drsimonjackson@gmail.com to get in touch.