British Journal of Ophthalmology

Identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...
European Medical Journal

Glucose-to-Potassium Ratio Signals AKI After Brain Injury

Glucose-to-potassium ratio shows a J-shaped link with AKI after traumatic brain injury, with high levels predicting increased ...
IEEE

Predictive Analysis of Network-Based Attacks by Hybrid Machine Learning Algorithms Utilizing Bayesian Optimization, Logistic Regression, and Random Forest Algorithm

Abstract: These days, intrusion detection systems are one of the newest trends in society. These technologies serve as a defense against a variety of security breaches, the number of which has been ...
IEEE

A Third-Order Majorization Algorithm for Logistic Regression With Convergence Rate Guarantees

Abstract: In this paper, we study the classical Logistic Regression (LR) problem in machine learning. Traditionally, the solving algorithms are based on either the first- or second-order approximation ...
Scientific Research Publishing

Credit Risk Modeling in Banking: A Comparative Analysis of Logistic Regression and Machine Learning Approaches ()

1 School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou, China. 2 School of Sciences, Zhejiang University of Science and Technology, ...
Investopedia

Predictive Modeling: Techniques, Uses, and Key Takeaways

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Jared Ecker is a researcher and fact-checker ...
Hosted on MSN

Logistic Regression Explained with Gradient Descent — Full Derivation Made Easy!

Struggling to understand how logistic regression works with gradient descent? This video breaks down the full mathematical derivation step-by-step, so you can truly grasp this core machine learning ...
Frontiers

Expectation-maximization alternating least squares for tensor network logistic regression

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...
technologynetworks

Logistic Regression

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...
technologynetworks

Linear Regression

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...
eWeek

What is Linear Regression? A Comprehensive Guide

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...