proxy.golang.org : github.com/ContextLogic/leaves
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.
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purl: pkg:golang/github.com/%21context%21logic/leaves
License: MIT
Latest release: over 5 years ago
First release: over 5 years ago
Namespace: github.com/ContextLogic
Last synced: 10 days ago