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Uncertainty Quantification in Machine Learning with an Easy Python Interface
- Uncertainty quantification in machine learning (ML) models is crucial for understanding the precision of predictions, especially in real-world applications.
- ML Uncertainty is a Python package that offers an intuitive interface to estimate uncertainties in ML predictions and model parameters using powerful mathematical methods.
- The package supports linear regression, LASSO regression, ridge regression, elastic net, regression splines, and non-linear regression.
- ML Uncertainty allows for uncertainty estimation in random forests by leveraging bootstrapping techniques to estimate prediction intervals.
- The ErrorPropagation class in ML Uncertainty computes uncertainty in response variables due to uncertainties in input variables and model parameters.
- ML Uncertainty distinguishes itself by enabling uncertainty propagation and catering to various models beyond linear regression.
- The package optimizes computational efficiency by separating model training from uncertainty quantification to offer reliable estimates.
- Future work involves expanding ML Uncertainty to other ML frameworks like PyTorch and TensorFlow, adding support for more models, and improving documentation.
- ML Uncertainty aims to enhance the reliability and interpretability of machine learning models by providing efficient uncertainty quantification tools.
- Contributions from the open-source community are encouraged to enhance the capabilities and applicability of the ML Uncertainty package.
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