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 ...
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 ...
GitHub

Issues: DanielCO1111/ML---Logistic-Regression-Classification-Models-

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Issues are used to track todos, bugs, feature requests, and more.
Scientific Research Publishing

Kirasich, K., Smith, T. and Sadler, B. (2018) Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review, 1, Article 9.

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Directions Magazine

Performing Cropland Land Cover Classification in QGIS Using Landsat 8

This tutorial outlines a complete workflow for classifying cropland land cover using Landsat 8 imagery and version 2.3.2 of the Semi-Automatic Classification Plugin (SCP) for QGIS. The study area is ...