Projects
benchm-ml
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 - Released: 28 Mar 2015 - 1,870
benchm-databases
A minimal benchmark of various tools (statistical software, databases etc.) for working with tabular data of moderately large sizes (interactive data analysis).
R - Released: 25 Feb 2015 - 90
benchm-dl
Playing with various deep learning tools and network architectures
Python - Released: 23 Oct 2016 - 69
xgboost-adv-workshop-LA
Advanced workshop on XGBoost with Tianqi Chen in Santa Monica, June 2, 2016
R - Released: 20 May 2016 - 26
ML-scoring
Compare the scoring speed of several open source machine learning libraries.
R - Released: 23 May 2017 - 20
kaggle-scripts-R-pydata
Kaggle scripts: R vs pydata + most popular R and Python packages for Machine Learning
R - Released: 22 Jan 2016 - 11
ml-algos-perf
Performance of Machine Learning Algorithms - playground for experimentation in order to understand their performance characteristics as a function of the attributes of the datasets used for training
Python - Released: 19 Aug 2016 - 7