scikit-rvm
A scikit-learn compatible Python implementation of the Relevance Vector Machine for sparse Bayesian learning.
What is scikit-rvm?
scikit-rvm is a Python library that implements the Relevance Vector Machine (RVM), a sparse Bayesian machine learning algorithm. It provides a scikit-learn compatible interface for both regression and classification tasks, offering probabilistic predictions and automatic model complexity control. The RVM serves as a Bayesian alternative to Support Vector Machines with typically sparser solutions.
Data scientists and machine learning practitioners who need probabilistic models with sparse representations and want to integrate them seamlessly into scikit-learn workflows. Researchers interested in Bayesian machine learning methods will also find it valuable.
Developers choose scikit-rvm because it provides the mathematical advantages of the Relevance Vector Machine—probabilistic outputs, sparser models, and automatic complexity control—through the familiar and well-documented scikit-learn API, making it easy to integrate into existing machine learning pipelines.
Overview
Relevance Vector Machine implementation using the scikit-learn API.
Use Cases
Best For
- Implementing sparse Bayesian alternatives to Support Vector Machines
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