scikit-learn-contrib/bde
Bayesian Deep Ensembles via MILE: easy to use, scikit-learn compatible and fast (JAX powered)
This tool helps data scientists and machine learning engineers create more reliable predictions and better understand the certainty of their models. It takes your standard tabular datasets for classification or regression tasks as input. The output provides not just predictions but also uncertainty estimates and credible intervals, making it easier to trust and act on the model's results. It's ideal for anyone who needs to quantify the confidence of their model's outputs for critical decision-making.
Use this if you are building classification or regression models on tabular data and need to understand the uncertainty or confidence level associated with each prediction.
Not ideal if your primary goal is simply to make predictions without needing to quantify the uncertainty, or if you are working with non-tabular data like images or text.
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41
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Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 04, 2026
Commits (30d)
0
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