Wraps the boosterPtr and guarantees that Native library is initialized everytime it is needed
Defines the Booster parameters passed to the LightGBM classifier.
Special parameter for classification model for actual number of classes in dataset
Defines common parameters across all LightGBM learners related to histogram bin construction.
Represents a LightGBM Booster learner
Represents a LightGBM Booster learner
Custom ComplexParam for LightGBMBooster, to make it settable on the LightGBM models.
Model produced by LightGBMClassifier.
Model produced by LightGBMClassifier.
Trains a LightGBM Classification model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.
Trains a LightGBM Classification model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms. For more information please see here: https://github.com/Microsoft/LightGBM. For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
Represents a LightGBM dataset.
Represents a LightGBM dataset. Wraps the native implementation.
Defines common LightGBM execution parameters.
Defines parameters for fraction across all LightGBM learners.
Defines common parameters across all LightGBM learners related to learning score evolution.
Contains common LightGBM model methods across all LightGBM learner types.
Defines parameters for LightGBM models
Defines common parameters across all LightGBM learners.
Defines common prediction parameters across LightGBM Ranker, Classifier and Regressor
Trains a LightGBMRanker model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.
Trains a LightGBMRanker model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms. For more information please see here: https://github.com/Microsoft/LightGBM. For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
Model produced by LightGBMRanker.
Model produced by LightGBMRanker.
Model produced by LightGBMRegressor.
Model produced by LightGBMRegressor.
Trains a LightGBM Regression model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.
Trains a LightGBM Regression model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms. For more information please see here: https://github.com/Microsoft/LightGBM. For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst Note: The application parameter supports the following values:
Defines parameters for slots across all LightGBM learners.
Defines the Booster parameters passed to the LightGBM ranker.
Defines the Booster parameters passed to the LightGBM regressor.
Defines the common Booster parameters passed to the LightGBM learners.
Helper utilities for LightGBM learners
Wraps the boosterPtr and guarantees that Native library is initialized everytime it is needed