Simple/Incomplete Benchmark of Machine Learning Libraries for Classification

The sharing for today :)

All benchmarks are wrong, but some are useful

"This project aims at a minimal benchmark for scalability, speed, and accuracy of commonly used implementations of a few machine learning algorithms. The target of this study is binary classification with numeric and categorical inputs (of limited cardinality i.e. not very sparse) and no missing data, perhaps the most common problem in business applications (e.g. credit scoring, fraud detection or churn prediction). If the input matrix is of n x p, n is varied as 10K, 100K, 1M, 10M, while p is ~1K (after expanding the categoricals into dummy variables/one-hot encoding). This particular type of data structure/size (the largest) stems from this author's interest in some particular business applications.

The algorithms studied are

• linear (logistic regression, linear SVM)
• random forest
• boosting
• deep neural network

in various commonly used open source implementations like

• R packages
• Python scikit-learn
• Vowpal Wabbit
• H2O
• xgboost
• Spark MLlib."

Author: Szilard

Access full repo at http://bit.ly/2os2J5H