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…

An investigation of San Francisco Fire Incidents using open data - exploratory analysis and modelling logit and multinomial logit regressions

This program uses the polarity and intensity of the words to assign one of five ratings to product reviews using Multinomial Logistic Regression. The data given was biased with more positive than negative reviews and hence required regularization.

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…

This project encompasses a range of neural and non-neural model implementations to classifiy MNIST digits. The goal is to compare the performance of each technique including details of hyper-parameters, training ans testing errors, training and testing duration and additional parameters used in the analysis.

Developed a random forest model to predict satellite images with 91.4% accuracy and 0.02% standard deviation of accuracies for all replications.

Given the title of a fake news article A and the title of a coming news article B, program classifies B into agree, disagree, and unrelated.

Choice-Based Conjoint Analysis, Multinomial Logit Model, Multinomial Logit Model with random coefficients

Métodos Estatísticos de Apoio à Decisão II

Given the title of a fake news article A and the title of a coming news article B, program classifies B into agree, disagree, and unrelated.

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 …

This was the code I use to process a Multinomial Logistic Regression on R, with the Apollo Choice Modeling Package for R. Used to calculate the utility function of particular customers of vehicles in Bogotá, Colombia

Modeling the relationship between fetal heart rate signals and fetal outcome using multinomial logistic regression

The Exame Nacional do Ensino Médio (also known as ENEM) is a national Brazilian standardized test that allows students to conquer a spot in universities in the country and abroad (Inep, 2016). With millions of examinees from different social backgrounds, this paper aims to use the socio-economic data gathered in the 2019 exam application to pred…

Multinomial logistic regression modeling used to determine influential factors in nicotine usage status

Automating Assumption Checks for Regression Models (Work in Progress, Currently Paused)

Inflated Discrete Choice Models

A MCMC Bayesian analysis versus Frequentist Analysis of Animal Crossing: New Horizons game players in-game behavior using a Multinomial Logistic Regression Model to adjust the original paper results.

UNCW BAN 502 This course explores methods for model selection, parameter estimation, and validation. Focused on techniques and algorithms from the statistical and machine learning disciplines.

This is a release of data and analysis scripts of the "Public Interest in Autonomous Vehicle Adoption: Evidence from the 2015, 2017, and 2019 Puget Sound Travel Surveys" paper published in Journal of Transportation Engineering Part A. The paper can be accessed at https://doi.org/10.1061/JTEPBS.0000655. All scripts are written in R.