Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Abstract: This paper's primary goal is to use machine learning techniques, specifically Logistic Regression and Decision Trees, to identify bogus news on social media. An innovative logistic model is ...

Matthew Berry, Jay Croucher and Connor Rogers won't label Philadelphia Eagles running back Saquon Barkley a potential fantasy bust in 2025, but they don't see him repeating last year's historic season ...

This project uses concepts from the TV show The Good Place to explore binary and multinomial logistic regression. The dataset contains behavioral features from 1,000 individuals—such as how often they ...

Logistic regression is the most cost-effective model for medial vascular calcification classification, with a mean ICER of $278 using five low-cost features. Despite similar diagnostic accuracy, ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

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 ...

Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population.

This project implements a Logistic Regression model trained using Stochastic Gradient Descent (SGD). The code includes functionality for training the model, evaluating its performance, and performing ...