Welcome to napkinXC’s documentation!

Note

Documentation is currently a work in progress!

napkinXC is an extremely simple and fast library for extreme multi-class and multi-label classification that implements the following methods both in Python and C++:

• Probabilistic Label Trees (PLTs) - for multi-label log-time training and prediction,

• Hierarchical softmax (HSM) - for multi-class log-time training and prediction,

• Binary Relevance (BR) - multi-label baseline,

• One Versus Rest (OVR) - multi-class baseline.

All the methods decompose multi-class and multi-label into the set of binary learning problems.

Right now, the detailed descirption of methods and their parameters can be found in this paper: Probabilistic Label Trees for Extreme Multi-label Classification

Contents:

• Python Quick Start
• Using C++ executable
• Python API

Indices and tables

• Index

• Search Page