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 ...
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 ...
Visual Studio Magazine

Decision Tree Regression from Scratch Without Pointers or Recursion 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 ...
IEEE

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine

Abstract: Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of ...
GitHub

lorepy: Logistic Regression Plots for Python

Lorepy offers distinct advantages over traditional methods like stacked bar plots. By employing a linear model, Lorepy captures overall trends across the entire feature range. It avoids arbitrary ...