In order to solve the problem of chronic heart failure risk prediction in the elderly, a logistic regression modeling framework with Bayesian method was proposed, aiming to solve the problem of ...

A new possibilistic logistic regression is investigated, which can be used in cases where the explanatory variables are crisp observations but the values of the response variable are non-precise and ...

lorepy: Logistic Regression Plots for Python Logistic Regression plots are used to plot the distribution of a categorical dependent variable in function of a continuous independent variable. If you ...

Script : Predict User satisfaction with Python. Contribute to pcadic/Logistic-Regression-Model development by creating an account on GitHub.

The Data Science Lab Logistic Regression from Scratch Using Raw Python The fundamental technique has been studied for decades, thus creating a huge amount of information and alternate variations that ...

The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity.

When the dependent variable is categorical, a common approach is to use logistic regression, a method that takes its name from the type of curve it uses to fit data.