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.).
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Tidy time series forecasting
- forecasting
Automate Data Exploration and Treatment
TranslatedScorecard Development in R, scorecard
- r
- scorecard
- credit-scoring
- release
Forecast the US demand for electricity
Random forests for R for large data sets, optimized with parallel tree-growing and disk-based memory
gateway for segmentation data to Rstats & visualization with ggplot2 - in development
- visualization
- fluorescence-data
- ggplot