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.
523 stars. No commits in the last 6 months.
Use this if you need to understand the uncertainty in your predictions for tasks like risk assessment, medical diagnostics, or financial forecasting.
Not ideal if your primary goal is maximum prediction accuracy without needing to interpret the model's confidence or robustness.
Stars
523
Forks
119
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 22, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AmazaspShumik/sklearn-bayes"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
tensorflow/probability
Probabilistic reasoning and statistical analysis in TensorFlow
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks....
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
google/edward2
A simple probabilistic programming language.