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An extension of XGBoost to probabilistic modelling
Created
2019-06-16
978 commits to master branch, last one 10 months ago
Python package for conformal prediction
python
sklearn
regression
scikit-learn
prediction-sets
jupyter-notebook
machine-learning
quantile-regression
confidence-intervals
conformal-prediction
conformal-regressors
prediction-intervals
conformal-classifiers
uncertainty-quantification
conformal-predictive-systems
cumulative-distribution-function
Created
2021-11-17
196 commits to main branch, last one 2 months ago
An extension of LightGBM to probabilistic modelling
Created
2020-07-03
194 commits to master branch, last one 8 months ago
Conformalized Quantile Regression
Created
2019-04-12
201 commits to master branch, last one 2 years ago
Quantile Regression Forests compatible with scikit-learn.
Created
2022-03-23
404 commits to main branch, last one 2 days ago
An extension of CatBoost to probabilistic modelling
Created
2019-12-20
44 commits to master branch, last one about a year ago
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
Created
2022-11-17
11 commits to main branch, last one about a year ago
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
Created
2024-03-01
31 commits to main branch, last one about a month ago
Valid and adaptive prediction intervals for probabilistic time series forecasting
Created
2022-02-11
26 commits to main branch, last one 2 years ago
Bringing back uncertainty to machine learning.
Created
2020-03-29
437 commits to main branch, last one 5 months ago
Adaptive Conformal Prediction Intervals (ACPI) is a Python package that enhances the Predictive Intervals provided by the split conformal approach by employing a weighting strategy.
Created
2023-02-03
18 commits to main branch, last one about a year ago