There are 1 repository under glmnet topic.
A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.
Accurate estimation and robust modelling of translation dynamics at codon resolution
Leap motion image recognition gesture checker GUI(C#) for developing Hand bone angles, Positions, and recognizable gestures(fuzzy logic) implementable in Virtual/Augmented reality apps.
MVPA tutorial - Rogers lab brain imaging unit
Survival learners for the `mlexperiments` R š¦
Algorithmes dāapprentissage et modĆØles statistiques: Un exemple de rĆ©gression logistique rĆ©gularisĆ©e et de validation croisĆ©e pour prĆ©dire le dĆ©crochage scolaire
stress detection in social networks
The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.
Advanced Regression Techniques to predict housing prices.
My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.
Detailed exploratory and predictive analysis of Airbnb data using R for data manipulation and model building.
A GAUSS wrapper of the glmnet package for fitting generalized linear models via penalized maximum likelihood.
we fit various splines to model the COVID-19 daily positive case numbers in Florida from 3/3/20 ā 3/7/21.
Feature Selection using Elastic net function in the glmnet R package
A multi-response Gaussian model capable of accurately estimating the composition of blood samples from their gene expression profiles. Fit on Affymetrix Gene ST gene expression profiles using the glmnet R package.
application of machine learning to indoor localization of RF sources
Estimate ILI (Influenza-Like Illness) levels in Italy by looking at Wikipedia usage.
Research project to measure the firm Expected Investment Growth (EIG) based on a combination of machine learning tools and text regression.
Comparison between the implementations of the Lasso algorithm between the Spark MLib library and the R glmnet package.
Over the past months, we have seen a significant racial justice reckoning happening across the country since the killing of George Floyd by a police officer in May 2020. This incident sparked a redirection of attention to similar lives that had been lost at the hands of officers, leading to calls for re-evaluation of the role and power that policeĀ hold. In order for stakeholders like activism groups and local policymakers to make the most change in the quickest and most effective manner in response to these calls, the data code and report strived to answer a questions that will enable this. The primary tool used was R, with ggplot and machine learning packages.
We use machine learning techniques for identification of the best cognitive markers for cocaine dependence.
Elastic Net, Lasso and Ridge models can be analyzed by the formula format.
Fake News analysis and prediction in R Script. Naive Bayes, Random Forest, SVM, NNET, ROC, Confusion Matrix, Accuracy, F1 score.
The study focuses on modeling and predicting H5N1 bird flu outbreaks in the United States at the county level, utilizing diverse statistical techniques and machine learning models.
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.