Uses of Class
org.apache.ignite.ml.composition.stacking.StackedModel

Packages that use StackedModel

Package	Description
org.apache.ignite.ml.composition.stacking	Contains classes used for training with stacking technique.

Uses of StackedModel in org.apache.ignite.ml.composition.stacking

Methods in org.apache.ignite.ml.composition.stacking that return StackedModel

Modifier and Type	Method and Description
<K,V> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) Trains model based on the specified data.
<K,V> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.update(StackedModel<IS,IA,O,AM> mdl, 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> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.updateModel(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

Methods in org.apache.ignite.ml.composition.stacking with parameters of type StackedModel

Modifier and Type	Method and Description
boolean	StackedDatasetTrainer.isUpdateable(StackedModel<IS,IA,O,AM> mdl) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.
<K,V> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.update(StackedModel<IS,IA,O,AM> mdl, 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> StackedModel<IS,IA,O,AM>	StackedDatasetTrainer.updateModel(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor) This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.