szilard/benchm-ml resource

Health v2 · maintenance only

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Adoption not part of the score

1895 stars · 327 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A minimal benchmark comparing scalability, speed, and accuracy of open-source machine learning implementations (R packages, scikit-learn, H2O, xgboost, lightgbm, Spark MLlib, Vowpal Wabbit) for binary classification. It varies dataset sizes from 10K to 10M rows across linear models, random forests, gradient boosting, and deep neural networks.

Use cases

• compare speed of xgboost vs lightgbm vs h2o on classification
• find out which ML library scales to 10M rows without running out of memory
• benchmark random forest implementations in R and Python
• evaluate accuracy of gradient boosting vs deep neural networks on tabular data
• choose an ML tool for credit scoring or fraud detection workloads
• reproduce a minimal ML benchmark with my own tool

When to choose

• you need evidence-based comparisons of classification libraries on tabular data
• you are evaluating ML tools for medium-to-large business datasets (10K-10M rows)
• you want a simple template to benchmark your own ML implementation

When to avoid

• you need benchmarks on sparse, high-cardinality, or missing-data scenarios
• you need distributed multi-node scaling results
• you need up-to-date results - much of the benchmark dates to 2015 and the author moved to a newer project
• you need benchmarks for tasks other than binary classification

Facets

dataset · maturity maintenance

benchmarking machine-learning data-science machine-learning data-science performance python jvm cross-platform binary-classification scikit-learn xgboost h2o spark-mllib r-packages gradient-boosting random-forest deep-neural-networks scalability-benchmark

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