💪 🤔 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

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Optimal topic identification from a pool of Latent Dirichlet Allocation models

A rolling version of the Latent Dirichlet Allocation.

R package for focused information criteria for model comparison

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

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

Data and function bundle for model selection guide for ecologists

Monte Carlo Penalty Selection for graphical lasso

Comparison of model selection methods for Boston dataset

D-probabilities of parametric models using nonparametric model reference

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…

comparing many classification algorithms (Naive Bayes, Logistic Regression, Support Vector Machine) on Spiral Data with tuning SVM's parameters with mentioning Decision Trees and K-Nearest Neighbors implementation.

The main objective is to understand the relationship between diffeent variable and testeing many Regression model and choosing the efficent one them predincting new points

Eficient Stepwise Selection in Decomposable Models