lsorber/neo-ls-svm
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
Implements automatic hyperparameter optimization for regularization and kernel parameters without manual tuning, plus conformal prediction with Bayesian uncertainty quantification for prediction intervals and quantiles. Built on Orthogonal Random Features for linear computational scaling, it solves LS-SVM in both primal and dual spaces while returning leave-one-out residuals and isotonically calibrated probabilities alongside predictions—with native pandas DataFrame integration for seamless data science workflows.
No commits in the last 6 months. Available on PyPI.
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34
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3
Language
Python
License
MIT
Category
Last pushed
Apr 01, 2024
Monthly downloads
25
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0
Dependencies
4
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