Transform features by scaling each feature to a given range. References from scikit-learn
A lightweight JavaScript library for essential feature engineering tasks in machine learning. Provides utilities for normalization, standardization, one-hot encoding and missing value handling. Designed for simplicity and performance in both Node.js and b
Web Component running an ONNX surrogate model of a respiratory physiology simulator at ~30 Hz in the browser.
Zero-dependency universal inference library for skjson models
A simple machine learning library in JavaScript/TypeScript
Preprocessing utilities for machine learning in Starlight
Use Python's #1 machine learning library from Node.js
A high-level API for the augurs forecasting library.
Preprocessing transformers for the ferrolearn ML framework
Time series classification and transformation library for Rust
Python bindings for sklears machine learning library using PyO3
Data pipeline and dataset utilities for TenfloweRS
scikit-learn-style machine learning umbrella crate — re-exports every anofox-ml-* subcrate under a single API
Clustering algorithms (KMeans, DBSCAN, HDBSCAN, GMM, BGMM, MeanShift, OPTICS, Birch, Spectral, AffinityPropagation, Agglomerative) for anofox-ml
Core traits and types for the anofox-ml machine learning library
Linear and Quadratic Discriminant Analysis for the anofox-ml library
Random forest and gradient boosting ensemble methods for the anofox-ml library
Gaussian Process regression + classification (Laplace) for the anofox-ml library
CSV data loading with ndarray integration for the anofox-ml machine learning library