Skip to content
#

binomial-logistic-regression

Here are 4 public repositories matching this topic...

This Python package enables to efficiently compute leave-one-out cross validation error for multinomial logistic regression with elastic net (L1 and L2) penalty. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time. MATLAB version: https://github.com/T-Obuchi/Accele…

  • Updated Aug 6, 2018
  • Python

Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either …

  • Updated Sep 11, 2021
  • Jupyter Notebook

This MATLAB package enables to efficiently compute leave-one-out cross validation error for multinomial logistic regression with elastic net (L1 and L2) penalty. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time. Python version: https://github.com/T-Obuchi/Accele…

  • Updated Aug 19, 2020
  • MATLAB

Improve this page

Add a description, image, and links to the binomial-logistic-regression topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the binomial-logistic-regression topic, visit your repo's landing page and select "manage topics."

Learn more