TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...
AHA/ASA Journals

Multistage Electrocardiographic Profiling for Mental Stress-Induced Myocardial Ischemia in Women: A Prospective, Age-Matched Cohort Study in China

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...
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 ...
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 ...
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 ...