The cumulative logistic regression model, also known as the proportional odds model (POM), is commonly used for analyzing ordinal data because of its effectiveness in providing generalizing ...

In a logistic regression model, the coefficients (represented by β in the equation) represent the log odds of the outcome variable being 1 for each one-unit increase in a particular explanatory ...

In this paper a novel nonlinear logistic regression model based on a simplex basis function neural network is introduced that outputs probability of categorical variables in response to multiple ...

This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and ...

Traditional logistic regression analysis is widely used in the binary classification problem, but it has many iterations and it takes a long time to train large amounts of data, which is not ...

Logistic Regression with KerasYou will see that linear Regression doesn’t perform well for the data points shown above because for x < 24, the model will predict class 1, hence making some errors as ...