Here's a `README.md` for your NPM package that includes the advanced fraud detection using machine learning models:
Declarative Specifications of Visual Analytics Processes
A Random Forest implementation for JavaScript supporting distributed computation
Machine Learning library for the web and Node
Machine Learning library for the web and Node
Random Forest with WebAssembly and WebWorkers
Random forest regressor and classifier
A simple random forest classifier.
A library for implementing custom decision trees and random forests
Machine Learning Algorithms in Rust
Machine learning framework with spatial modeling, conformal prediction, and gradient boosting competitive with C++
High-performance Gradient Boosted Decision Tree engine for large-scale tabular data
High-performance SIMD compute library with GPU support, LLM inference engine, and GGUF model loading (was: trueno)
Decision trees and random forests for Rust. Presently a WIP
Decision tree and ensemble models for the ferrolearn ML framework
Machine learning models available for FlexiML framework
Scivex — Classical machine learning: trees, ensembles, clustering, pipelines
Ensemble methods for sklears: Random Forest, Gradient Boosting, AdaBoost
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