He Couldn’t Land a Job Interview. Was AI to Blame?

Armed with some Python and a white-hot sense of injustice, one medical student spent six months trying to figure out whether ...
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

SarahRoWo/Logistic_Regression_Python_MI_Paper

This set of notebooks enables the analysis of comorbidities associated with male infertility using structured EHR data. First, we identified nonoverlapping patients with male infertility and patients ...
Scientific Research Publishing

Harrell, Jr. (2015) Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd Edition, Springer.

ABSTRACT: There is a set of points in the plane whose elements correspond to the observations that are used to generate a simple least-squares regression line. Each value of the independent variable ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

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 ...
IEEE

Logistic Regression-Based Example Selection for Enhanced Few-Shot Learning in Intent Classification

Abstract: This study introduces a novel approach to example selection in few-shot learning scenarios for dialog intent classification, leveraging logistic regression to refine the set of examples ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...