Iterated Racing for Automatic Algorithm Configuration

Hyperparameter optimization package of the mlr3 ecosystem

R interface to Keras Tuner

Performs Variables selection and model tuning for Species Distribution Models (SDMs). It provides also several utilities to display results.

Flexible Bayesian Optimization in R

Black-box optimization framework for R.

Successive Halving and Hyperband in the mlr3 ecosystem

Collection of search spaces for hyperparameter optimization in the mlr3 ecosystem

Machine Learning Hyper-parameter Tuning processes

An extensible framework for reproducible machine learning experiments

An R-toolkit for model evaluation, model comparison and hyperparameter tuning based on cross-validation

This R-based data science project on the UCI Parkinson's dataset employs machine learning (Decision tree, Random Forest, SVM, XGBoost) with a focus on hyperparameter tuning and feature selection. This repository showcases insights into Parkinson's disease prediction using effective data science practices.

Data mining and machine learning libraries are used in this machine learning project to detect the fraud. More importantly, this report focuses on vehicle insurance company claim statistics to use the gathered knowledge from actuarial Science Course.

This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"

An R Package to return a variety of different model types, complete with hyper-parameter tuning

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

This project aims to predict customer churn using machine learning techniques in R. By analyzing customer data, we identify patterns and build models to help businesses improve customer retention strategies.

Machine learning models build on real time data

Instructional materials (course files) for the BBT4206 course (Business Intelligence II) using R. Topic: Hyperparameter Tuning.

This R project analyzes the 'Preventive Maintenance for Marine Engines' dataset from Kaggle. The dataset includes engine parameters, maintenance events, and failure indicators, aiming to predict maintenance needs and identify potential failures