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pypi.org : mlrl-seco
A scikit-learn implementation of a separate-and-conquer multi-label rule learning algorithm
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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-boomer 0.10.0
A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label ...7 versions - Latest release: about 1 month ago - 1 dependent package - 1 dependent repositories - 307 downloads last month - 0 stars on GitHub - 1 maintainer
mlrl-common 0.10.0
Provides common modules to be used by different types of multi-label rule learning algorithms7 versions - Latest release: about 1 month ago - 3 dependent packages - 1 dependent repositories - 139 downloads last month - 0 stars on GitHub - 1 maintainer
mlrl-testbed 0.10.0
Provides utilities for the training and evaluation of multi-label rule learning algorithms7 versions - Latest release: about 1 month ago - 1 dependent repositories - 13 downloads last month - 0 stars on GitHub - 1 maintainer