szilard/benchm-ml

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. 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. If your software tool of choice is not here, your can benchmark it with minimal work with the following instructions.)