bayesian-machine-learning and sklearn-bayes
About bayesian-machine-learning
krasserm/bayesian-machine-learning
Notebooks about Bayesian methods for machine learning
This collection of notebooks helps machine learning practitioners understand and implement Bayesian methods. It takes raw data, such as sensor readings or survey responses, and processes it through various Bayesian models like regression, classification, and optimization. The output includes predictions with quantified uncertainty and optimized parameters, which are crucial for data scientists, statisticians, and researchers building robust predictive systems.
About sklearn-bayes
AmazaspShumik/sklearn-bayes
Python package for Bayesian Machine Learning with scikit-learn API
This package helps data scientists and machine learning engineers build predictive models that can quantify uncertainty. You provide your dataset, and it outputs models capable of making predictions along with confidence levels. This is especially useful for those who need more than just a prediction, but also an understanding of how reliable that prediction is.
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