"I want you to explain the 'why' behind GWR and Random Forest results"—Creating maps that interpret spatial statistics and machine learning model results for field operations

Introduction"I finally built a model with good accuracy using GWR or Forest-based Regression, but when I showed it to my boss, they asked, 'I understand this district is high, but why is it high?' and ...

“I only have point data for machine learning explanatory variables” — Rasterizing distance, density, and proximity for use as predictive model inputs

Introduction“I want to predict demand using GWR or Random Forest, but all I have is point data like facility locations or ...
Visual Studio Magazine

Random Forest Regression Using C#

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...
European Coatings

Non-invasive AI tool identifies pigments in historical paintings

Researchers have developed a non-invasive machine learning approach that classifies painting pigments using images acquired ...
BIOENGINEER

Machine Learning Leads Soil Mapping Race, but No Single Model Wins Everywhere

A first-of-its-kind systematic review of 190 studies finds that HASM, Euclidean-enhanced machine learning, and Bayesian ...
Visual Studio Magazine

Random Forest Regression and Bagging Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...