HiClass: a Python library for local hierarchical classification compatible with scikit-learn
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
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.
Results and benchmarks
HiClass is an open-source Python library for local hierarchical classification entirely compatible with scikit-learn.
Benchmark evidence is limited
Evidence graph: 3 refs, 3 links.
Utility signals: depth 70/100, grounding 75/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
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No verified maintained repo yet
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Time to first repro: a few days
scikit-learn-contrib/hiclass is the closest maintained adjacent implementation (Strong overlap with paper title keywords). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 138 GitHub stars.
- Adjacent implementations are not paper-verified
- Recommended repository is adjacent and not paper-verified.
- Adjacent implementation match confidence is low.
Reproduction readiness
No repo
No verified implementation available
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Repositories and ecosystem
Closest related implementations
These are not paper-verified. Use them as reference points when no direct implementation is available.
- scikit-learn-contrib/hiclass Adjacent · Confidence: Low · 138 stars
Strong overlap with paper title keywords
No additional verified repositories beyond the primary recommendation.
Hugging Face artifacts
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Research context
21
Citations
0
References
Tasks
Python (programming language), Documentation, Computer science, Implementation, MIT License, License, Extensibility, Programming language
Methods
None detected
Domains
Biochemistry, Genetics and Molecular Biology
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