IEEE

Fuzzy LASSO Logistic Regression: A Regularized Framework for Interpretable Binary Classification Under Uncertainty

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Hosted on MSN

Build logistic regression in Python from scratch easily

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...
Frontiers

A machine learning-based predictive nomogram for early neurological improvement after thrombolysis in acute ischemic stroke

Background: Early neurological improvement (ENI) is a critical prognostic indicator for acute ischemic stroke (AIS) patients undergoing intravenous thrombolysis with recombinant tissue plasminogen ...
GitHub

This is a beginner data analytics project with model building.

There was an error while loading. Please reload this page. This project applies Logistic Regression to predict whether a passenger aboard the Titanic survived. It ...
Scientific Research Publishing

Dreiseitl, S. and Ohno-Machado, L. (2002) Logistic Regression and Artificial Neural Network Classification Models: A Methodology Review. Journal of Biomedical Informatics, 35 ...

ABSTRACT: A degenerative neurological condition called Parkinson disease (PD) that evolves progressively, making detection difficult. A neurologist requires a clear healthcare history from the ...
Scientific Research Publishing

Zhu, W., Qiu, R. and Fu, Y. (2024) Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks. arXiv: 2401.09682.

ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...