Package | Description |
---|---|
org.apache.ignite.ml.composition |
Contains classes for ensemble of models implementation.
|
org.apache.ignite.ml.composition.bagging |
Contains bootstrap aggregation (bagging) trainer allowing to combine some other trainers and
return a bagged version of them.
|
org.apache.ignite.ml.composition.predictionsaggregator |
Contains classes for several predictions aggregation strategies
working with predictions vector from models ensemble.
|
org.apache.ignite.ml.trainers |
Contains model trainers.
|
Modifier and Type | Method and Description |
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PredictionsAggregator |
ModelsComposition.getPredictionsAggregator()
Returns predictions aggregator.
|
PredictionsAggregator |
ModelsCompositionFormat.predictionsAggregator() |
Constructor and Description |
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ModelsComposition(List<? extends IgniteModel<Vector,Double>> models,
PredictionsAggregator predictionsAggregator)
Constructs a new instance of composition of models.
|
ModelsCompositionFormat(List<IgniteModel<Vector,Double>> models,
PredictionsAggregator predictionsAggregator)
Creates an instance of ModelsCompositionFormat.
|
Constructor and Description |
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BaggedTrainer(DatasetTrainer<? extends IgniteModel,L> tr,
PredictionsAggregator aggregator,
int ensembleSize,
double subsampleRatio,
int featuresVectorSize,
int featureSubspaceDim)
Construct instance of this class with given parameters.
|
Modifier and Type | Class and Description |
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class |
MeanValuePredictionsAggregator
Predictions aggregator returning the mean value of predictions.
|
class |
OnMajorityPredictionsAggregator
Predictions aggregator returning the most frequently prediction.
|
class |
WeightedPredictionsAggregator
Predictions aggregator returning weighted plus of predictions.
|
Modifier and Type | Method and Description |
---|---|
static <L> BaggedTrainer<L> |
TrainerTransformers.makeBagged(DatasetTrainer<? extends IgniteModel,L> trainer,
int ensembleSize,
double subsampleRatio,
PredictionsAggregator aggregator)
Add bagging logic to a given trainer.
|
static <M extends IgniteModel<Vector,Double>,L> |
TrainerTransformers.makeBagged(DatasetTrainer<M,L> trainer,
int ensembleSize,
double subsampleRatio,
int featureVectorSize,
int featuresSubspaceDim,
PredictionsAggregator aggregator)
Add bagging logic to a given trainer.
|
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024