public class RegressorTrainingNode extends AbstractTrainingNode<Regressor>
Modifier and Type | Class and Description |
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static class |
RegressorTrainingNode.InvertedData
Tuple containing an inverted dataset (i.e., feature-wise not exmaple-wise).
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AbstractTrainingNode.LeafDeterminer
DEFAULT_SIZE, depth, greaterThan, impurityScore, leafDeterminer, lessThanOrEqual, numExamples, split, splitID, splitValue
Constructor and Description |
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RegressorTrainingNode(RegressorImpurity impurity,
RegressorTrainingNode.InvertedData tuple,
int dimIndex,
String dimName,
int numExamples,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<Regressor> outputInfo,
AbstractTrainingNode.LeafDeterminer leafDeterminer) |
Modifier and Type | Method and Description |
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List<AbstractTrainingNode<Regressor>> |
buildTree(int[] featureIDs,
SplittableRandom rng,
boolean useRandomSplitPoints)
Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
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Node<Regressor> |
convertTree()
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double |
getImpurity()
The impurity score of this node.
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float |
getWeightSum()
The sum of the weights associated with this node's examples.
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static RegressorTrainingNode.InvertedData |
invertData(Dataset<Regressor> examples)
Inverts a training dataset from row major to column major.
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copy, createSplitNode, getDepth, getNextNode, getNumExamples, isLeaf, shouldMakeLeaf
public RegressorTrainingNode(RegressorImpurity impurity, RegressorTrainingNode.InvertedData tuple, int dimIndex, String dimName, int numExamples, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputInfo, AbstractTrainingNode.LeafDeterminer leafDeterminer)
public double getImpurity()
Node
public float getWeightSum()
AbstractTrainingNode
getWeightSum
in class AbstractTrainingNode<Regressor>
public List<AbstractTrainingNode<Regressor>> buildTree(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints)
buildTree
in class AbstractTrainingNode<Regressor>
featureIDs
- Indices of the features available in this split.rng
- Splittable random number generator.useRandomSplitPoints
- Whether to choose split points for features at random.public Node<Regressor> convertTree()
convertTree
in class AbstractTrainingNode<Regressor>
public static RegressorTrainingNode.InvertedData invertData(Dataset<Regressor> examples)
examples
- An input dataset.InvertedFeature
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