RF
A parallel Random Forest implementation in Go for classification and regression tasks.
What is RF?
RF.go is a Go implementation of the Random Forest algorithm, an ensemble learning method used for classification and regression tasks. It builds multiple decision trees during training and outputs the mode or mean prediction of individual trees, providing robust and accurate machine learning models.
Go developers and data scientists looking for a performant, concurrent Random Forest library to integrate into machine learning applications.
It offers parallel tree training using goroutines for efficient multi-core utilization, model persistence via JSON, and support for both classification and regression, making it a practical choice for Go-based ML projects.
Overview
Random Forest implemtation in GoLang
Use Cases
Best For
- Building classification models with high accuracy requirements
- Implementing regression analysis in Go applications
Related Projects
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