leaves
Prediction with gradient boosting regression tree models is implemented in leaves, a Go software library.
About leaves
leaves is a Go library that implements prediction for GBRT (Gradient Boosting Regression Trees) models in pure Go, so Go programs can use models from LightGBM, XGBoost, and scikit-learn without C API bindings. It supports parallel predictions for batches, multiclass predictions, sigmoid and softmax transformations, missing values for XGBoost, and retrieval of decision-tree leaf indices.
Limits stated in the source include transformation support restricted to sigmoid and softmax, experimental scikit-learn support limited to GradientBoostingClassifier in pickle protocol 0, and an API subject to change before the 1.0.0 release.
Key features
- Run GBRT prediction in pure Go without C bindings
- Support parallel predictions for batches
- Support multiclass predictions for LightGBM and XGBoost
- Read LightGBM text and JSON models including gbdt, rf, dart
- Read XGBoost binary models including gbtree, gblinear, dart
- Read experimental scikit-learn GradientBoostingClassifier pickle protocol 0
Best for: Go developers who need to run LightGBM, XGBoost, or scikit-learn tree-model predictions in Go programs.
Good for
- Works in pure Go without C API bindings
- Reads LightGBM, XGBoost, and scikit-learn model inputs
- Supports parallel batch predictions and multiclass predictions
Watch out for
- API is subject to change before 1.0.0 release
- Transformation functions limited to sigmoid and softmax
- scikit-learn support is experimental and limited to GradientBoostingClassifier pickle protocol 0
leaves at a glance
- Website
- github.com/dmitryikh/leaves
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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leaves FAQ
Which model files can leaves read?
LightGBM models from text and JSON format, XGBoost models from binary format, and scikit-learn models from pickle format protocol 0.
Which model types are supported?
LightGBM gbdt, rf and dart; XGBoost gbtree, gblinear and dart; and sklearn.ensemble.GradientBoostingClassifier as experimental support.
Does leaves require C bindings?
No. It implements prediction code for GBRT models in pure Go.
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