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.
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
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 ...
GitHub

TfELM : Extreme Learning Machines Framework with Python and TensorFlow

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...
GitHub

Depyler – Compiles Python to Rust

A Python-to-Rust transpiler with semantic verification and memory safety analysis. Depyler translates annotated Python code into idiomatic Rust, preserving program semantics while providing ...
IEEE

A Study on Sailboat Price Prediction Method Based on Multiple Linear Regression Model and XGBoost Algorithm

Abstract: this study aims to explore sailboat price prediction methods through the utilization of a multiple linear regression model and the XGBoost algorithm, addressing the pricing challenges in the ...
IEEE

House Price Prediction Using Linear and Lasso Regression

Abstract: This research paper aims to predict house prices with more efficiency and accuracy. The research paper involves three machine learning algorithms, "linear regression, lasso regression, and ...