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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

Intercepts of Newton-Cholesky logistic regression get corrupted when warm starting

When using multinomial logistic regression with warm starts from a previous iteration, the final coefficients in the model are correct, but the intercepts somehow get filled with incorrect numbers ...
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 ...
Frontiers

Multifactorial drivers of engagement in sex work among Ethiopian women: a multinomial logistic regression approach

Department of HIV and Tuberculosis Research, Ethiopian Public Health Institute, Addis Ababa, Ethiopia Background: Understanding the multifactorial drivers of female sex workers' (FSWs) engagement in ...
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 ...
Frontiers

Monitoring pilots’ mental workload in real flight conditions using multinomial logistic regression with a ridge estimator

Piloting an aircraft is a cognitive task that requires continuous verbal, visual, and auditory attentions (e.g., Air Traffic Control Communication). An increase or decrease in mental workload from a ...
IEEE

Improving the Performance of Multinomial Logistic Regression in Vowel Recognition by Determining Best Regression Coefficients

Abstract: The performance of Multinomial Logistic Regression (MLR) is highly dependent on the estimated value of its parameters (Regression Coefficients - RCs). However, the usual maximum likelihood ...