Hosted on MSN

How to Validate Your Data With Statistical Tests in Python

Statistical testing in Python offers a way to make sure your data is meaningful. It only takes a second to validate your data ...
Visual Studio Magazine

Linear Regression Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...
C&EN

pK a Data-Driven Insights into Multiple Linear Regression Hydrolysis QSARs: Applicability to Perfluorinated Alkyl Esters

Oak Ridge Institute for Science and Education (ORISE), Hosted at U.S. Environmental Protection Agency, Athens, Georgia 30605, United States ...
Visual Studio Magazine

Linear Support Vector Regression Using C# with Particle Swarm Training

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...
Hosted on MSN

Multiple Linear Regression in Python from Scratch ¦ Explained Simply

In this video, we will implement Multiple Linear Regression in Python from Scratch on a Real World House Price dataset. We will not use built-in model, but we will make our own model. This can be a ...
IEEE

Representation Transfer Learning via Multiple Pre-Trained Models for Linear Regression

Abstract: In this paper, we consider the problem of learning a linear regression model on a data domain of interest (target) given few samples. To aid learning, we are provided with a set of ...
Frontiers

Objective quantification of sound sensory attributes in side-by-side vehicles using multiple linear regression models

The evaluation of sound quality is a significant area of research in audio and acoustics (Lyon, 2003). Traditional methods for assessing vehicle sound quality involve both subjective and objective ...
IEEE

Spectral Universality in Regularized Linear Regression With Nearly Deterministic Sensing Matrices

Abstract: It has been observed that the performances of many high-dimensional estimation problems are universal with respect to underlying sensing (or design) matrices. Specifically, matrices with ...