InfoWorld

Machine learning: Demystifying linear regression and feature selection

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems can ...

2025 In-Depth Interpretation: Advanced DID, Machine Learning, and Cutting-Edge Literature on Regression Discontinuity

In 2025, the field of quantitative research continues to thrive, with new research findings emerging constantly. This article will focus on the recent advanced DID (Difference-in-Differences), machine ...
Visual Studio Magazine

Kernel Ridge Regression with Cholesky Inverse Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...
ascopubs.org

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams

TRAIL Score: A Simple Model to Predict Immunochemotherapy Tolerability in Patients With Diffuse Large B-Cell Lymphoma We trained models using logistic regression (LR) and four commonly used ML ...
Visual Studio Magazine

Nadaraya-Watson Kernel Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression using the C# language. NW kernel regression is simple to implement and is ...

In-Depth Analysis for 2025: Advanced DID, Machine Learning, and Cutting-Edge Literature on Regression Discontinuity

This article will focus on the recent cutting-edge research literature on advanced DID (Difference-in-Differences), machine learning, and Regression Discontinuity (RD), providing researchers with a ...