Multilabel Classification Python Packages

iterative-stratification

scikit-learn cross validators for iterative stratification of multilabel data

mlrl-common

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-boomer

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-seco

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

annif

Annif is a multi-algorithm automated subject indexing tool for libraries, archives and museums.

libmultilabel

This library has been moved to https://github.com/ntumlgroup/LibMultiLabel

pyss3

A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:

mlrl-testbed

Provides utilities for the training and evaluation of machine learning algorithms

torchblocks-chen

A PyTorch-based toolkit for natural language processing

mlrl-testbed-sklearn

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-testbed-arff

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-util

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

semantic-f1-score

Semantic F1 Score for Multi-label Classification

mlrl-testbed-slurm

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

torchblocks

A PyTorch-based toolkit for natural language processing

evalne

Source code for EvalNE, a Python library for evaluating Network Embedding methods.

fastxml

FastXML / PFastXML / PFastreXML - Implementation of Extreme Multi-label Classification

example-wise-f1-maximizer

A scikit-learn meta-estimator for multi-label classification that aims to maximize the example-wise F1 measure

maestros

A package for splitting multilabel datasets into train and test sets, while preserving the distribution of labels and keeping samples from the same group together. Includes a report and chart for visualizing the stratification.