The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
IEEE

LYRICEL: Knowledge Graphs Combined With Large Language Models and Machine Learning for Cross-Cultural Analysis of Lyrics—The Case of Greek Songs

Abstract: This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and ...
UNDP

Launching the Roadmap for Validation of Non-Formal and Informal Learning in Albania

Tirana, 17 December 2025 – Albania has taken a step forward in building an inclusive lifelong learning system by convening a national consultation on the Roadmap for the Implementation of the ...
Frontiers

Interpretable machine learning for cognitive impairment prediction in Parkinson’s disease: a multicenter validation study with SHAP analysis

1 Key Laboratory of Neuromolecular Biology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China 2 Department of Neurology, The ...
Hosted on MSN

Which Cross-Validation Method Should You Use in Machine Learning?

Learn how to choose the right cross-validation method for your machine learning projects. Compare techniques like k-fold, stratified, and leave-one-out cross-validation, and understand when to use ...
Hosted on MSN

Why You Shouldn’t Always Use K-Fold Cross Validation in Machine Learning

K-Fold cross-validation is popular, but it’s not always the best choice. Learn when K-Fold works, when it can mislead your results, and explore alternative validation strategies for more reliable ...
GitHub

sms-spam

This project classifies SMS messages as spam or ham using a PyTorch logistic regression model with a bag-of-words representation, including train/validation/test split, performance evaluation ...
IEEE

Learning Software Overtime Estimation From Experts‘ Annotations: A Greedy Cross-Validation-Based Machine Learning Approach

Abstract: Effective overtime planning in software projects remains a critical challenge due to the complex interplay between project constraints and human-centric decision-making. This study ...
C&EN

Discovery of Potential Tyrosinase Inhibitors via Machine Learning and Molecular Docking with Experimental Validation of Activity and Skin Permeation

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Natural product libraries represent an ideal source for ...