Ecosyste.ms: Packages
An open API service providing package, version and dependency metadata of many open source software ecosystems and registries.
Top 1.9% dependent packages on proxy.golang.org
Top 2.5% dependent repos on proxy.golang.org
Top 3.2% forks on proxy.golang.org
proxy.golang.org : github.com/dmitryikh/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.
Registry
-
Source
- Documentation
- JSON
purl: pkg:golang/github.com/dmitryikh/leaves
Keywords: boosting, decision-trees, go, golang, lightgbm, machine-learning, xgboost
License: MIT
Latest release: 11 months ago
First release: over 3 years ago
Namespace: github.com/dmitryikh
Dependent packages: 8
Dependent repositories: 4
Stars: 372 on GitHub
Forks: 61 on GitHub
See more repository details: repos.ecosyste.ms
Last synced: 19 days ago