Multilabel Classification Python Packages

iterative-stratification

scikit-learn cross validators for iterative stratification of multilabel data

mlrl-boomer

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

mlrl-common

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:

torchblocks-chen

A PyTorch-based toolkit for natural language processing

mlrl-util

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

mlrl-testbed

Provides utilities for the training and evaluation of machine learning algorithms

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-testbed-slurm

Adds support for the Slurm Workload Manager to the package "mlrl-testbed"

semantic-f1-score

Semantic F1 Score: Fair Evaluation Under Fuzzy Class Boundaries

torchblocks

A PyTorch-based toolkit for natural language processing

evalne

Open Source Network Embedding Evaluation toolkit

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

Multi-label stratified splits, while preserving group independence. Includes a stratification chart and report.