aksnzhy/xlearn

Health v2 · maintenance only

Adoption not part of the score

3090 stars · 516 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

xLearn is a high-performance C++ machine learning package implementing linear models (LR), factorization machines (FM), and field-aware factorization machines (FFM), with Python and CLI interfaces. It is optimized for large-scale sparse data, offering out-of-core training, cross-validation, and early stopping.

Use cases

• train factorization machines on click-through rate data
• predict CTR for online advertising
• build recommendation models from sparse user-item features
• train FFM models for Kaggle-style competitions
• fit logistic regression on high-dimensional sparse features
• train models on datasets larger than memory via out-of-core learning

When to choose

• you need fast FM/FFM training on large sparse datasets
• you are replacing liblinear, libfm, or libffm with a faster alternative
• you want a simple Python or CLI interface without heavy dependencies
• your data is too big for memory and you need out-of-core training

When to avoid

• you need deep learning or neural network models
• you need GPU acceleration
• you need actively developed features or recent community support
• you need dense-data or general-purpose ML beyond linear/FM models

Facets

library · maturity maintenance

machine-learning machine-learning data-science cpp python cli cross-platform factorization-machines field-aware-factorization-machines linear-models sparse-data libffm libfm click-through-prediction recommendation

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