Package | Description |
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org.apache.ignite.ml.tree.randomforest |
Contains random forest implementation classes.
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org.apache.ignite.ml.tree.randomforest.data.impurity |
Contains implementation of impurity computers based on histograms.
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org.apache.ignite.ml.tree.randomforest.data.statistics |
Contains implementation of statistics computers for Random Forest.
|
Modifier and Type | Method and Description |
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List<RandomForestTreeModel> |
RandomForestModel.getModels()
Returns containing models.
|
protected ArrayList<RandomForestTreeModel> |
RandomForestTrainer.initTrees(Queue<TreeNode> treesQueue)
Creates list of trees.
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Modifier and Type | Method and Description |
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protected RandomForestModel |
RandomForestRegressionTrainer.buildComposition(List<RandomForestTreeModel> models)
Returns composition of built trees.
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protected abstract RandomForestModel |
RandomForestTrainer.buildComposition(List<RandomForestTreeModel> models)
Returns composition of built trees.
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protected RandomForestModel |
RandomForestClassifierTrainer.buildComposition(List<RandomForestTreeModel> models)
Returns composition of built trees.
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Constructor and Description |
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RandomForestModel(List<RandomForestTreeModel> oldModels,
PredictionsAggregator predictionsAggregator) |
Modifier and Type | Method and Description |
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Map<NodeId,ImpurityHistogramsComputer.NodeImpurityHistograms<S>> |
ImpurityHistogramsComputer.aggregateImpurityStatistics(ArrayList<RandomForestTreeModel> roots,
Map<Integer,BucketMeta> histMeta,
Map<NodeId,TreeNode> nodesToLearn,
Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)
Computes histograms for each feature.
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Modifier and Type | Method and Description |
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void |
LeafValuesComputer.setValuesForLeaves(ArrayList<RandomForestTreeModel> roots,
Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)
Takes a list of all built trees and in one map-reduceImpurityStatistics step collect statistics for evaluating
leaf-values for each tree and sets values for leaves.
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Ignite Database and Caching Platform : ver. 2.12.0 Release Date : January 10 2022