Package | Description |
---|---|
org.apache.ignite.ml.composition.boosting |
Contains Gradient Boosting regression and classification abstract classes
allowing regressor type selecting in child classes.
|
org.apache.ignite.ml.tree.boosting |
Contains implementation of gradient boosting on trees.
|
Modifier and Type | Method and Description |
---|---|
<K,V> GDBModel |
GDBTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Trains model based on the specified data.
|
static GDBModel |
GDBModel.fromJSON(Path path)
Loads RandomForestModel from JSON file.
|
protected <K,V> GDBModel |
GDBTrainer.updateModel(GDBModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
GDBModel |
GDBModel.withLblMapping(IgniteFunction<Double,Double> internalToExternalLblMapping) |
Modifier and Type | Method and Description |
---|---|
protected @NotNull List<IgniteModel<Vector,Double>> |
GDBLearningStrategy.initLearningState(GDBModel mdlToUpdate)
Restores state of already learned model if can and sets learning parameters according to this state.
|
boolean |
GDBTrainer.isUpdateable(GDBModel mdl) |
<K,V> List<IgniteModel<Vector,Double>> |
GDBLearningStrategy.update(GDBModel mdlToUpdate,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer
updates model in according to new data and return new model.
|
protected <K,V> GDBModel |
GDBTrainer.updateModel(GDBModel mdl,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> preprocessor)
Gets state of model in arguments, update in according to new data and return new model.
|
Modifier and Type | Method and Description |
---|---|
<K,V> List<IgniteModel<Vector,Double>> |
GDBOnTreesLearningStrategy.update(GDBModel mdlToUpdate,
DatasetBuilder<K,V> datasetBuilder,
Preprocessor<K,V> vectorizer)
Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer
updates model in according to new data and return new model.
|
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Ignite Database and Caching Platform : ver. 2.10.0 Release Date : March 10 2021