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 ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates Random Forest ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...
Visual Studio Magazine

Decision Tree Regression from Scratch with Pointers 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 ...
Scientific Research Publishing

Predicting Primary School Student Dropout Risk: A Machine Learning Framework for Early Intervention ()

Student dropout in primary education is a critical global challenge with significant long-term societal and individual consequences. Early identification of at-risk students is a crucial first step ...
Morningstar

Machine Learning Can Forecast Which Stock Factors Will Outperform

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
Frontiers

Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis

Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). Novel computational methods leveraging machine learning (ML) architectures ...
IEEE

Predictive Analytics for Better Diabetic Patient Care: Insights from XGBoost, Random Forest, and Logistic Regression

Abstract: This study presents a data-driven framework using machine learning algorithms to address three key aspects of diabetes management: diagnosis, hospital readmission prediction, and healthcare ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...
EurekAlert!

Neuromorphic devices and machine learning combine to make brain-like devices possible

As modern manufacturing increasingly relies on artificial intelligence (AI), automation, and real-time data processing, the need for faster and more energy-efficient computing systems has never been ...
Live Science

Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...