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Linear regression gradient descent explained simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...
IEEE

A Data-driven Method for DRT Function Reconstruction Based on Ridge Regression and Genetic Algorithm

Abstract: Electrochemical impedance spectroscopy (EIS) technology is widely applied in the field of lithium-ion battery research. The distribution of relaxation times (DRT) method, as a novel approach ...
Visual Studio Magazine

Decision Tree Regression from Scratch with Pointers Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...
GitHub

ols-regression

Excel portfolio project combining Fama–French 3-factor regressions with Markowitz optimization. Calculates expected returns, covariances, and solves for Minimum Variance & Tangency Portfolios.
GitHub

.net 10 regression: Different output for algorithm using IBinaryInteger Zero and One #121572

Description In the process of upgrading from .net 9 to .net 10 I have seen one of my tests fail. It utilizes the IBinaryInteger<T> interface with known values of One and Zero to generate a polynomial ...
IEEE

A Linear Surrogate-Based Algorithm for Fitting Gaussian Mixture Functions

Abstract: Gaussian Mixture Function (GMF) is a widely utilized model for analyzing and elucidating experimental data in science and engineering, where the fitting of GMF with noisy observations is ...