dmitryikh/leaves
pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
When you have a machine learning model built with popular frameworks like LightGBM or XGBoost and you need to deploy it within an application written in Go, 'leaves' helps you do that efficiently. It takes your pre-trained model file (like a .txt or binary file) and allows your Go program to use it to make predictions. This is ideal for developers building Go applications that need to integrate existing gradient boosting models.
470 stars. No commits in the last 6 months.
Use this if you are a Go developer building an application that needs to perform predictions using pre-trained LightGBM, XGBoost, or scikit-learn Gradient Boosting models without relying on C API bindings.
Not ideal if you need to train new models or if your Go application requires extensive transformation functions beyond sigmoid and softmax for predictions.
Stars
470
Forks
86
Language
Go
License
MIT
Category
Last pushed
Jul 03, 2024
Commits (30d)
0
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