A few years ago I came across a method for reading academic papers which I've kept coming back to as a reliable systematic approach to efficiently read important papers of varying complexity. The method itself comes from a paper by Prof. Srinivasan Keshav, an ACM Fellow and researcher at the
This post gives an overview of LightGBM and aims to serve as a practical reference. A brief introduction to gradient boosting is given, followed by a look at the LightGBM API and algorithm parameters.
A key concern when dealing with cyclical features is how we can encode the values such that it is clear to the deep learning algorithm that the features occur in cycles. This post looks at a strategy to encode cyclical features in order to clearly express their cyclical nature.