A scikit-based Python environment for performing multi-label classification[1] What’s Python? Python is a high-level programming language. Essentially a scripting language, it does not require compilations, and provide data scientists a very intuitive and fast working environment through its edit-test-debug cycle. Featuring simple and easy-to-learn syntax, Python can be used in examples as simple as the example shown below(calculation of tips), or as heavy-lifting as multi-dimensional time series analytics or the gluing language of pre-existing compiled components written in Python or other languages.
A scikit-based Python environment for performing multi-label classification[1] What was done in this study? Building on the existing Python 3 sckikit-learn library, researchers from Poland recently published a new multi-label classification library, scikit-multilearn (skml in short) on Journal of Machine Learning Research. Compatible with sciki-learn and scipy ecosystems, this library provides native Python implementations of mutli-label classification methods, allowing the single-label deep learning methods to be extended into multi-label tasks - which is particularly efficient in problem transformation than other established libraries.