A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

Advanced High Performance Data Science Toolbox for R by Laurae

🔍🐦🤖 Detect Twitter Bots!

R interface for XGBoost on Spark

R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process

光伏短期功率预测大赛 代码

Animated Visualizations of Popular Machine Learning Algorithms

Stock prediction using xgboost and knn classification done in R

🌳 Stacked Gradient Boosting Machines

The Fashion-MNIST dataset and machine learning models.

A small wrapper package that enables full survival curve estimation using xgboost

Data and Code to reproduce results for my talk at Paris: R in Insurance 2017 Conference

GCAP (Gene-level Circular Amplicon Prediction) firstly implements extrachromosomal DNA detection from whole-exome-sequencing (WES) data and absolute copy number profiles. https://shixiangwang.r-universe.dev/gcap

Kaggle Grupo Bimbo Inventory Demand, NO.156/top 8% (post competition analysis) 庫存需求預測、前 8 % ( 賽後分析 )

Predict respiratory patient mortality in ICU units using the MIMIC III database

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

Kaggle Bosch Production Line Performance, NO.74/top 6% (post competition analysis) 生產線分析、前 6 % ( 賽後分析 )

R package to impute methylation within WGBS using machine learning

Discrete SuperLearner with Grid-Search for Longitudinal Data