PyTorch implementation of Deep Ordinal Regression Network for Monocular Depth Estimation

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms.

Ordinal Regression and Classification Algorithms

Ordinal Regression tutorial for the International Summer School on Deep Learning 2019

Ordinal regression models in PyTorch

Ordinal regression in Python

A Python/C++ implementation of Bayesian Factorization Machines

Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)

An attempt to diagnose Alzheimer's disease earlier

Tensorflow implementations of ordinal regression

Deep Ordinal Regression with Label Diversity

Surrogate residuals for cumulative link and general regression models in R

Ordinal Regression for mlr3

Methods for clm objects

Raw files for a document that provides an overview of models for the case of a categorical dependent variable.

Ordinal sequential regression models in R

Implementation of the Conway-Maxwell-Binomial distribution (for unimodal/ordinal classification problems) in PyTorch.

This repository should help people that would like to code in R and work with the National Health and Nutrition Examination Survey (NHANES). Some topics corved are SQL , logistic regression.... etc

Undergraduate final project: Ordinal Clasification with Residual Networks for the Adience dataset.

A machine learning project to classify the number of stars associated with a yelp review based only on the message. Uses the open yelp data set.

This repository is on Machine Learning using Python 3.8.3 using Visual Studio Code. Most of the programs are from IBM Machine Learning course and some algorithms (course out of scope) are presenterd only for learning purpose. These codes will be on topics like Regression, Classification, Clustering and Recommender Systems.

Assignments from Microeconometrics

Doubly robust estimators of ordinal treatment effects

Course of Categorical Data Analysis at UEM in 2018

Power Link Functions in Modeling Dependent Ordinal Data

Generic implementation for Generalized Linear Models including Logistic, Poisson and Ordinal Regression for Classification purposes

The Informational Content of Geographical Indications

A Python Implementation of Kernel Extreme Learning Machine for Ordinal Regression

Predicting the european soccer championships matches with ordinal regression, using qualifier data