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Conformal classifiers, regressors and predictive systems

Quantile Forest ⭐ 70

Quantile Regression Forests compatible with scikit-learn.

Liquidsvm ⭐ 45

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-s

Bringing back uncertainty to machine learning.

Im2im Uq ⭐ 29

Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.

Quantregforest ⭐ 8

R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).

Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems

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