szilard/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.).
This project helps data professionals understand which open-source machine learning tools are best suited for building accurate and scalable binary classification models. It takes various datasets (up to 10 million rows and 1,000 features) and applies common algorithms like random forests, boosting, and neural networks using tools like H2O, xgboost, and scikit-learn. The output provides insights into training time, memory usage, and predictive accuracy (AUC) for each tool and dataset size. It's intended for data scientists, machine learning engineers, and analysts who need to choose the right tools for large-scale classification problems in business applications like credit scoring or fraud detection.
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Use this if you need to select an appropriate open-source machine learning library or framework for a new binary classification project, especially when working with medium to large datasets (millions of rows) and needing to balance speed, memory, and accuracy on a single machine.
Not ideal if your primary goal is interpretability of the models, if you need to compare different algorithms' theoretical foundations, or if you are focused on highly sparse data or use cases beyond binary classification.
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1,894
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330
Language
R
License
MIT
Category
Last pushed
Sep 16, 2022
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