#170170th of 206 in ML frameworks and libraries
linfa
Rust toolkit of classical ML algorithms, modeled on scikit-learn
- Stars
- 4.8k
- License
- Apache-2.0
- Last commit
- Aug 2026
- Last release
- Dec 2025
- Language
- Rust
4,589 on 22 Apr 20264,769 on 11 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Linfa is a collection of Rust crates implementing preprocessing and classical machine learning algorithms, in the spirit of scikit-learn. It covers clustering, linear and logistic regression, SVMs, decision trees, ensembles, PCA and t-SNE, each as a separate sub-crate. It uses a pure-Rust linear algebra backend by default, with optional OpenBLAS, Netlib or Intel MKL.
Who it is for: Rust developers who need scikit-learn-style classical ML in native code
Strengths
- Modular crates: depend only on the algorithms you need, such as clustering or svm
- Pure-Rust default backend, with optional OpenBLAS, Netlib or Intel MKL via features
- Browser WASM on wasm32-unknown-unknown through the wasm-bindgen feature
- Broad classical coverage: K-Means, DBSCAN, random forest, AdaBoost, PCA, t-SNE, TF-IDF
Weaknesses
- No neural network or deep learning crate in the algorithm list
- Logistic regression is described as two-class only
- OpenBLAS and Netlib backends are listed for Linux only
- Rust only, with no Python bindings mentioned
What it needs
- no GPU
- Needs Rust toolchain, optional OpenBLAS/Netlib/Intel MKL
- Models: K-Means, DBSCAN, random forest, SVM, linear regression
What changed
From the release notes. No flag means the note does not say so, not that the upgrade is safe.
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