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.
IEEE

Analysis of Linear Regression Using Different Optimization Techniques

Abstract: This paper gives an analysis of linear regression using different optimization techniques, including Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent. It ...
IEEE

Convergence of Online Identification for Mixed Linear Regression with Two Components

Abstract: This paper investigates the online identification and data clustering problems for mixed linear regression (MLR) model with two components, including the symmetric MLR, and the asymmetric ...
marktechpost

A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow

In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline ...