Package | Description |
---|---|
org.tribuo.classification.dtree.impl |
Provides internal implementation classes for classification decision trees.
|
org.tribuo.common.tree |
Provides common functionality for building decision trees, irrespective
of the predicted
Output . |
org.tribuo.regression.rtree.impl |
Provides internal implementation classes for the regression trees.
|
Modifier and Type | Class and Description |
---|---|
class |
ClassifierTrainingNode
A decision tree node used at training time.
|
Modifier and Type | Method and Description |
---|---|
Node<Label> |
ClassifierTrainingNode.convertTree()
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractTrainingNode<T extends Output<T>>
Base class for decision tree nodes used at training time.
|
class |
LeafNode<T extends Output<T>>
An immutable leaf
Node that can create a prediction. |
class |
SplitNode<T extends Output<T>>
An immutable
Node with a split and two child nodes. |
Modifier and Type | Method and Description |
---|---|
abstract Node<T> |
AbstractTrainingNode.convertTree()
Converts a tree from a training representation to the final inference time representation.
|
Node<T> |
SplitNode.copy() |
Node<T> |
Node.copy()
Copies the node and it's children.
|
Node<T> |
AbstractTrainingNode.copy() |
Node<T> |
SplitNode.getGreaterThan()
The node used if the value is greater than the splitValue.
|
Node<T> |
SplitNode.getLessThanOrEqual()
The node used if the value is less than or equal to the splitValue.
|
Node<T> |
SplitNode.getNextNode(SparseVector e)
Return the appropriate child node.
|
Node<T> |
Node.getNextNode(SparseVector example)
Returns the next node in the tree based on the supplied example, or null if it's a leaf.
|
Node<T> |
LeafNode.getNextNode(SparseVector e) |
Node<T> |
AbstractTrainingNode.getNextNode(SparseVector example) |
Constructor and Description |
---|
SplitNode(double splitValue,
int featureID,
double impurity,
Node<T> greaterThan,
Node<T> lessThanOrEqual)
Constructs a split node with the specified split value, feature id, impurity and child nodes.
|
SplitNode(double splitValue,
int featureID,
double impurity,
Node<T> greaterThan,
Node<T> lessThanOrEqual)
Constructs a split node with the specified split value, feature id, impurity and child nodes.
|
Modifier and Type | Class and Description |
---|---|
class |
JointRegressorTrainingNode
A decision tree node used at training time.
|
class |
RegressorTrainingNode
A decision tree node used at training time.
|
Modifier and Type | Method and Description |
---|---|
Node<Regressor> |
RegressorTrainingNode.convertTree()
|
Node<Regressor> |
JointRegressorTrainingNode.convertTree()
|
Copyright © 2015–2021 Oracle and/or its affiliates. All rights reserved.