The project involves the multivariate regression analysis of a dataset.
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Updated
Oct 24, 2020 - R
The project involves the multivariate regression analysis of a dataset.
Developed a linear regression model to forecast case shipments considering various predictor variables such as time trends (month), seasonality (seasonal index), and promotions. Conducted Durbin Watson test and generated a forecast and prediction interval.
Analysis of Predictive inference with jackknife+, a new method for creating prediction intervals with stronger coverage guarantees
Plotting of the Confidence interval and Prediction interval for a Linear Regression model.
Complete mathematical and statistical analysis of linear regression model
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.
Uncertainty quantification of black hole mass estimation
An HR predictive analytics tool for forecasting the likely range of a worker’s future job performance using multiple ANNs with custom loss functions.
implementation of fair dummies
DualAQD: Dual Accuracy-quality-driven Prediction Intervals
Prediction intervals for trees using conformal intervals. Docs at https://pitci.readthedocs.io/en/latest/
Implementation of Conformal Convolution T-learner (CCT) and Conformal Monte Carlo (CMC) learner
Deep joint mean and quantile regression for spatio-temporal problems
**curve_fit_utils** is a Python module containing useful tools for curve fitting
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
Prediction Intervals with specific value prediction
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.
Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
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.
Bringing back uncertainty to machine learning.
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