# HiClass: a Python library for local hierarchical classification compatible with scikit-learn

arXiv: https://arxiv.org/abs/2112.06560 · HTML: https://mlsanity.com/paper/2112.06560/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios](https://mlsanity.com/paper/2004.05835/) (2020)
- [Hierarchical Text Classification of Urdu News using Deep Neural Network](https://mlsanity.com/paper/2107.03141/) (2021)

## Built on by (most cited first, 5 of 5)

- [HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning](https://mlsanity.com/paper/2412.04661/) (2024)
- [A Benchmark Dataset for Multimodal Prediction of Enzymatic Function Coupling DNA Sequences and Natural Language](https://mlsanity.com/paper/2407.15888/) (2024)
- [MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrieval](https://mlsanity.com/paper/2401.16520/) (2024)
- [Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures](https://mlsanity.com/paper/2412.08228/) (2024)
- [Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach](https://mlsanity.com/paper/2508.20013/) (2025)

## Abstract (arXiv)

HiClass is an open-source Python library for local hierarchical classification entirely compatible with scikit-learn. It contains implementations of the most common design patterns for hierarchical machine learning models found in the literature, that is, the local classifiers per node, per parent node and per level. Additionally, the package contains implementations of hierarchical metrics, which are more appropriate for evaluating classification performance on hierarchical data. The documentation includes installation and usage instructions, examples within tutorials and interactive notebooks, and a complete description of the API. HiClass is released under the simplified BSD license, encouraging its use in both academic and commercial environments. Source code and documentation are available at https://github.com/scikit-learn-contrib/hiclass.