github.com/dmitryikh/leaves (go): Package leaves is pure Go implemetation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks.  General All loaded models exibit the same interface from `Ensemble struct`. One can use method `Name` to get string representation of model origin. Possible name values are "lightgbm.gbdt", "lightgbm.rf", "xgboost.gbtree", "xgboost.gblinear", etc.  Example: binary classification  build_breast_cancer_model.py:  predict_breast_cancer_model.go:  Output:  example: Multiclass Classification  build_iris_model.py  predict_iris_model.go:  Output:  Please note that one must not provide nEstimators = 0 when predict with DART models from xgboost. For more details see xgboost's documentation.  Models trained with 'boosting_type': 'dart' options can be loaded with func `leaves.LGEnsembleFromFile`. But the name of the model (given by `Name()` method) will be 'lightgbm.gbdt', because LightGBM model format doesn't distinguish 'gbdt' and 'dart' models.
Keywords: boosting|decision-trees|go|golang|lightgbm|machine-learning|xgboost