rizbicki/UQ4ML
Repository for the book Machine Learning Learning Beyond Point Predictions: Uncertainty Quantification, by Rafael Izbicki.
When you use machine learning models, it's often not enough to just get a single prediction; you need to understand how confident that prediction is. This resource helps you move beyond basic predictions to understand the range of possible outcomes and the certainty of your model's forecasts. It's for data scientists, statisticians, and researchers who build and apply machine learning models in critical decision-making contexts.
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Use this if you need to quantify the reliability and potential variability of your machine learning model's predictions, rather than just getting a single best guess.
Not ideal if you are only looking for basic machine learning model training or simple point predictions without any need for uncertainty estimates.
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Last pushed
Jul 04, 2025
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