💪 🤔 Modern Super Learning with Machine Learning Pipelines

Regression model building and forecasting in R

Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

Optimal topic identification from a pool of Latent Dirichlet Allocation models

A rolling version of the Latent Dirichlet Allocation.

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Exercises From Book "Applied Predictive Modeling" by "Kuhn and Johnson (2013)"

R package for focused information criteria for model comparison

Determine a Prototype from a number of runs of Latent Dirichlet Allocation.

# kaefa kwangwoon automated exploratory factor analysis for improving research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment

This project predicts tuition rates for U.S. public and private universities using linear regression with leave-one-out cross-validation. Helping to assess if a college market price, maximizing ROI and minimizing student loan debt.

Data and function bundle for model selection guide for ecologists

An R package for Augmented Backward Elimination

Determining the number of factors in Poisson factor model via thinning cross-validation. Stable release available on CRAN (https://cran.r-project.org/package=tcv); development version hosted on GitHub.

Monte Carlo Penalty Selection for graphical lasso

Comparison of model selection methods for Boston dataset

This project explores linear regression model selection in R using Best Subset Selection (BIC), stepwise methods with cross-validation, Ridge, and Lasso. Includes MSE evaluation on test data, multicollinearity analysis (VIF), and correlation insights for variable selection.

In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…