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

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 ...

Background: The purpose of this study is to use a variety of machine learning (ML) algorithms to build a risk prediction model for nursing students’ social anxiety, select the optimal model, and ...

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...

These were split into categories and their correlation with hypertension in this cohort was assessed using multivariate logistic regression. Python with libraries Numpy, Pandas, Scipy, Statsmodels, ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

Background: This study aimed to evaluate the predictive utility of routine hematological, inflammatory, and metabolic markers for bacteremia and to compare the classification performance of logistic ...

ABSTRACT: This study addresses the research question: What is the effect of higher education liberalization on the regional distribution of universities and educational attainment in Zambia? The ...

Python libraries are pre-written collections of code designed to simplify programming by providing ready-made functions for specific tasks. They eliminate the need to write repetitive code and cover ...