Machine learning identifies factors that may determine the age of onset of Huntington's disease

A team from the Faculty of Medicine and Health Sciences and the Institute of Neurosciences at the University of Barcelona ...
Daily Excelsior

Machine Learning Methods Used for Portfolio Optimization and Risk Management

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...
Open Heart

Machine learning-based cardiovascular risk calculator for non-cardiac surgery

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
GitHub

Predicting the Sale Price of Bulldozers using Machine Learning (scikit-learn, RandomForestRegressor and Hyperparameter tuning)

There was an error while loading. Please reload this page. In this notebook, we're going to go through an example machine learning project to use the characteristics ...
IEEE

A Review of Hyperparameter Tuning Methods in Machine Learning

Abstract: Hyperparameter tuning is a crucial process in the machine learning (ML) pipeline, as the performance of a learning algorithm is highly influenced by its hyperparameter configuration. This ...
Frontiers

Predictive effect of postoperative recovery in general anesthesia patients using interpretable models based on swarm intelligence machine learning

1 Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China 2 Sir Run Run Shaw Hospital Affiliated to Zhejiang University School of Medicine Alar Hospital, ...
GitHub

An AutoML framework for classical machine learning algorithms, automating model selection and hyperparameter tuning through Bayesian optimization, portfolio-based meta-learning ...

Start a Ray head node Connect and start Ray worker nodes via SSH Activate virtual environments and configure PYTHONPATH on all nodes 📌 Before running the script, ensure passwordless SSH access from ...
Scientific Research Publishing

Mohammadagha, M. (2025) Hyperparameter Optimization Strategies for Tree-Based Machine Learning Models Prediction: A Comparative Study of Ada-Boost, Decision Trees, and Random ...

ABSTRACT: This study presents a comprehensive and interpretable machine learning pipeline for predicting treatment resistance in psychiatric disorders using synthetically generated, multimodal data.