Understanding Homoskedasticity in Regression Modeling With Examples

Discover the importance of homoskedasticity in regression models, where error variance is constant, and explore examples that illustrate this key concept.
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
The Manila Times

Bin Li Contributes to Environmental Planning Through Data Analysis Frameworks for Climate Policy Implementation

A data-driven environmental planning framework strengthens climate governance through quality control standards, interdisciplinary expertise, and predictive emission modeling. By integrating ...
IEEE

Comparative Analysis of Flood Prediction Using Linear Regression

Abstract: Flood prediction as we known is a important role in reducing the impacts of effective disasters . This paper says that a linear regression-based model is designed for forecasting flood ...
GitHub

SCONE: Scaling Embedding Layers in Language Models

This implementation is part of LLMs Research, where I break down ~100 papers weekly and build the ones worth building. This repository contains an implementation of the SCONE (Scalable, Contextualized ...
IEEE

Incremental Heteroscedastic Gaussian Process Regression and Its Applications in Model Predictive Control

Abstract: Gaussian process regression (GPR) models are becoming increasingly tightly integrated into robotic systems, particularly in the context of robot model predictive control (MPC) operating in ...