Scientific Research Publishing

Wilson, D.L. (1994) The Analysis of Survival (Mortality) Data: Fitting Gompertz, Weibull, and Logistic Functions. Mechanisms of Ageing and Development, 74, 15-33.

ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...
Hosted on MSN

Logistic Regression Cost Function ¦ Machine Learning ¦ Simply Explained

Learn what is Logistic Regression Cost Function in Machine Learning and the interpretation behind it. Logistic Regression Cost function is "error" representation of the model. It shows how the model ...
Hosted on MSN

Build Logistic Regression From Scratch In Python – You Won'T Believe How Easy It Is!

Learn how to implement Logistic Regression from scratch in Python with this simple, easy-to-follow guide! Perfect for beginners, this tutorial covers every step of the process and helps you understand ...
IEEE

Segmenting Online Shoppers: A Combined Cluster and Logistic Regression Approach for Forecasting Purchase Behavior

Abstract: This paper examines customers’ online shopping behavior and its relationship to purchase decisions. There are two research questions for the paper: 1) How can marketers segment prior ...
Scientific Research Publishing

Zaidi, A. and Al Luhayb, A.S.M. (2023) Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression. Mathematical Problems in ...

ABSTRACT: Road traffic accidents are one of the global safety and socioeconomic challenges. According to WHO (2024), it has caused over 1.19 million annual fatalities. It is also projected to cause ...
Frontiers

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients

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.