rustlearn
A Rust crate providing efficient implementations of common machine learning algorithms with support for dense and sparse data.
What is rustlearn?
rustlearn is a machine learning crate for Rust that implements common algorithms like logistic regression, support vector machines, decision trees, random forests, and factorization machines. It solves the need for performant, native ML tools in Rust, offering competitive accuracy and speed compared to Python libraries. The crate supports both dense and sparse data, cross-validation, and parallel computation.
Rust developers and data scientists who need efficient machine learning implementations within Rust applications, particularly those requiring integration with Rust systems or performance-sensitive deployments.
Developers choose rustlearn for its native Rust performance, comprehensive algorithm coverage, and seamless handling of sparse and dense data. Its API is designed to be straightforward while providing competitive accuracy with scikit-learn, making it a robust choice for ML in Rust ecosystems.
Overview
Machine learning crate for Rust
Use Cases
Best For
- Building machine learning models directly in Rust applications
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