Package | Description |
---|---|
weka.classifiers.trees.j48 | |
weka.classifiers.trees.lmt |
Modifier and Type | Class and Description |
---|---|
class |
BinC45Split
Class implementing a binary C4.5-like split on an attribute.
|
class |
C45Split
Class implementing a C4.5-type split on an attribute.
|
class |
GraftSplit
Class implementing a split for nodes added to a tree during grafting.
|
class |
NBTreeNoSplit
Class implementing a "no-split"-split (leaf node) for naive bayes
trees.
|
class |
NBTreeSplit
Class implementing a NBTree split on an attribute.
|
class |
NoSplit
Class implementing a "no-split"-split.
|
Modifier and Type | Method and Description |
---|---|
ClassifierSplitModel |
C45ModelSelection.selectModel(Instances data)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
NBTreeModelSelection.selectModel(Instances data)
Selects NBTree-type split for the given dataset.
|
abstract ClassifierSplitModel |
ModelSelection.selectModel(Instances data)
Selects a model for the given dataset.
|
ClassifierSplitModel |
BinC45ModelSelection.selectModel(Instances data)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
C45ModelSelection.selectModel(Instances train,
Instances test)
Selects C4.5-type split for the given dataset.
|
ClassifierSplitModel |
NBTreeModelSelection.selectModel(Instances train,
Instances test)
Selects NBTree-type split for the given dataset.
|
ClassifierSplitModel |
ModelSelection.selectModel(Instances train,
Instances test)
Selects a model for the given train data using the given test data
|
ClassifierSplitModel |
BinC45ModelSelection.selectModel(Instances train,
Instances test)
Selects C4.5-type split for the given dataset.
|
Constructor and Description |
---|
C45PruneableClassifierTreeG(ModelSelection toSelectLocModel,
Instances data,
ClassifierSplitModel gs,
boolean prune,
float cf,
boolean raise,
boolean isLeaf,
boolean relabel,
boolean cleanup)
Constructor for pruneable tree structure.
|
Distribution(Instances source,
ClassifierSplitModel modelToUse)
Creates a distribution according to given instances and
split model.
|
Modifier and Type | Class and Description |
---|---|
class |
ResidualSplit
Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the
splitting criterion based on residuals of the LogitBoost algorithm.
|
Modifier and Type | Method and Description |
---|---|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances train)
Method not in use
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances data,
double[][] dataZs,
double[][] dataWs)
Selects split based on residuals for the given dataset.
|
ClassifierSplitModel |
ResidualModelSelection.selectModel(Instances train,
Instances test)
Method not in use
|
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