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pypi.org : mlrl-seco

A scikit-learn implementation of a separate-and-conquer multi-label rule learning algorithm

Registry - Source - Documentation - JSON
purl: pkg:pypi/mlrl-seco
Keywords: machine learning, scikit-learn, multi-label classification, rule learning, separate-and-conquer, gradient-boosting, machine-learning, multilabel-classification, rule-learning
License: MIT
Latest release: about 1 month ago
First release: about 1 month ago
Dependent packages: 1
Downloads: 467 last month
Stars: 0 on GitHub
Forks: 0 on GitHub
See more repository details: repos.ecosyste.ms
Last synced: 5 days ago

mlrl-testbed 0.10.0

Provides utilities for the training and evaluation of multi-label rule learning algorithms
7 versions - Latest release: about 1 month ago - 1 dependent repositories - 13 downloads last month - 0 stars on GitHub - 1 maintainer