**curve_fit_utils** is a Python module containing useful tools for curve fitting
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Updated
Dec 23, 2017 - Python
**curve_fit_utils** is a Python module containing useful tools for curve fitting
Deep joint mean and quantile regression for spatio-temporal problems
Complete mathematical and statistical analysis of linear regression model
The project involves the multivariate regression analysis of a dataset.
This module contains functions, bootStrapParamCI and bootStrapPredictInterval, that follow a bootstrap approach to produce confidence intervals for model parameters and prediction intervals for individual point predictions, respectively.
Prediction Intervals with specific value prediction
Prediction intervals for trees using conformal intervals. Docs at https://pitci.readthedocs.io/en/latest/
implementation of fair dummies
Plotting of the Confidence interval and Prediction interval for a Linear Regression model.
Conformalized Quantile Regression
Valid and adaptive prediction intervals for probabilistic time series forecasting
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
Official implementation of the paper "PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction" by Eli Simhayev, Gilad Katz and Lior Rokach.
Analysis of Predictive inference with jackknife+, a new method for creating prediction intervals with stronger coverage guarantees
An HR predictive analytics tool for forecasting the likely range of a worker’s future job performance using multiple ANNs with custom loss functions.
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.
Uncertainty quantification of black hole mass estimation
An extension of CatBoost to probabilistic modelling
Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
An extension of XGBoost to probabilistic modelling
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