The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data ... Transactions of ...

This study offers a comparative analysis of several ordinal activation functions that utilize four key cumulative link functions used in ordinal regression: logit (logistic distribution ... of this ...

Multicollinearity exists when there is a correlation between multiple independent variables in a multiple regression model. This can adversely affect the regression results. A variance inflation ...

One year of weather data (temperature, pressure, humidity, sunshine, evaporation, cloud cover, wind direction, and wind speed) from Canberra, Australia, has been used to develop the logistic ...

regression learning (RL)-enabled detection, and mathematical morphology (MM)-based refinement. First, the raw wind speed-power data are preprocessed via a set of simple yet efficient physical rules to ...

An ordered logistic regression model suitable for analysing the severity of diabetes as an ordinal outcome was used. This study relies on secondary data, which do not capture detailed clinical ...

Implementing binary / multiple logistic regression models, for the well known mnist dataset while also creating the support vector machine(SVM) models ...

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient ...