|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD | |||||||||
java.lang.Objectweka.classifiers.Classifier
weka.classifiers.trees.FT
public class FT
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves. The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values.
For more information see:
Joao Gama (2004). Functional Trees.
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.
@article{Gama2004,
author = {Joao Gama},
booktitle = {Machine Learning},
number = {3},
pages = {219-250},
title = {Functional Trees},
volume = {55},
year = {2004}
}
@article{Landwehr2005,
author = {Niels Landwehr and Mark Hall and Eibe Frank},
booktitle = {Machine Learning},
number = {1-2},
pages = {161-205},
title = {Logistic Model Trees},
volume = {95},
year = {2005}
}
Valid options are:
-B Binary splits (convert nominal attributes to binary ones)
-P Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
-I <numIterations> Set fixed number of iterations for LogitBoost (instead of using cross-validation)
-F <modelType> Set Funtional Tree type to be generate: 0 for FT, 1 for FTLeaves and 2 for FTInner
-M <numInstances> Set minimum number of instances at which a node can be split (default 15)
-W <beta> Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
-A The AIC is used to choose the best iteration.
| Field Summary | |
|---|---|
static int |
MODEL_FT
model types |
static int |
MODEL_FTInner
|
static int |
MODEL_FTLeaves
|
static Tag[] |
TAGS_MODEL
possible model types. |
| Fields inherited from interface weka.core.Drawable |
|---|
BayesNet, Newick, NOT_DRAWABLE, TREE |
| Constructor Summary | |
|---|---|
FT()
Creates an instance of FT with standard options |
|
| Method Summary | |
|---|---|
java.lang.String |
binSplitTipText()
Returns the tip text for this property |
void |
buildClassifier(Instances data)
Builds the classifier. |
double |
classifyInstance(Instance instance)
Classifies an instance. |
double[] |
distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names |
java.lang.String |
errorOnProbabilitiesTipText()
Returns the tip text for this property |
boolean |
getBinSplit()
Get the value of binarySplits. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
boolean |
getErrorOnProbabilities()
Get the value of errorOnProbabilities. |
double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure |
int |
getMinNumInstances()
Get the value of minNumInstances. |
SelectedTag |
getModelType()
Get the type of functional tree model being used. |
int |
getNumBoostingIterations()
Get the value of numBoostingIterations. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
TechnicalInformation |
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on. |
boolean |
getUseAIC()
Get the value of useAIC. |
double |
getWeightTrimBeta()
Get the value of weightTrimBeta. |
java.lang.String |
globalInfo()
Returns a string describing classifier |
java.lang.String |
graph()
Returns graph describing the tree. |
int |
graphType()
Returns the type of graph this classifier represents. |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] argv)
Main method for testing this class |
int |
measureNumLeaves()
Returns the number of leaves in the tree |
int |
measureTreeSize()
Returns the size of the tree |
java.lang.String |
minNumInstancesTipText()
Returns the tip text for this property |
java.lang.String |
modelTypeTipText()
Returns the tip text for this property |
java.lang.String |
numBoostingIterationsTipText()
Returns the tip text for this property |
void |
setBinSplit(boolean c)
Set the value of binarySplits. |
void |
setErrorOnProbabilities(boolean c)
Set the value of errorOnProbabilities. |
void |
setMinNumInstances(int c)
Set the value of minNumInstances. |
void |
setModelType(SelectedTag newMethod)
Set the Functional Tree type. |
void |
setNumBoostingIterations(int c)
Set the value of numBoostingIterations. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setUseAIC(boolean c)
Set the value of useAIC. |
void |
setWeightTrimBeta(double n)
Set the value of weightTrimBeta. |
java.lang.String |
toString()
Returns a description of the classifier. |
java.lang.String |
useAICTipText()
Returns the tip text for this property |
java.lang.String |
weightTrimBetaTipText()
Returns the tip text for this property |
| Methods inherited from class weka.classifiers.Classifier |
|---|
debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
public static final int MODEL_FT
public static final int MODEL_FTLeaves
public static final int MODEL_FTInner
public static final Tag[] TAGS_MODEL
| Constructor Detail |
|---|
public FT()
| Method Detail |
|---|
public Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilities
public void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class Classifierdata - the data to train with
java.lang.Exception - if classifier can't be built successfully
public double[] distributionForInstance(Instance instance)
throws java.lang.Exception
distributionForInstance in class Classifierinstance - the instance to compute the distribution for
java.lang.Exception - if distribution can't be computed successfully
public double classifyInstance(Instance instance)
throws java.lang.Exception
classifyInstance in class Classifierinstance - the instance to classify
java.lang.Exception - if instance can't be classified successfullypublic java.lang.String toString()
toString in class java.lang.Objectpublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class Classifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-B Binary splits (convert nominal attributes to binary ones)
-P Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
-I <numIterations> Set fixed number of iterations for LogitBoost (instead of using cross-validation)
-F <modelType> Set Funtional Tree type to be generate: 0 for FT, 1 for FTLeaves and 2 for FTInner
-M <numInstances> Set minimum number of instances at which a node can be split (default 15)
-W <beta> Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
-A The AIC is used to choose the best iteration.
setOptions in interface OptionHandlersetOptions in class Classifieroptions - the list of options as an array of strings
java.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class Classifierpublic double getWeightTrimBeta()
public boolean getUseAIC()
public void setWeightTrimBeta(double n)
public void setUseAIC(boolean c)
c - Value to assign to useAIC.public boolean getBinSplit()
public boolean getErrorOnProbabilities()
public int getNumBoostingIterations()
public SelectedTag getModelType()
public void setModelType(SelectedTag newMethod)
c - Value corresponding to tree type.public int getMinNumInstances()
public void setBinSplit(boolean c)
c - Value to assign to binarySplits.public void setErrorOnProbabilities(boolean c)
c - Value to assign to errorOnProbabilities.public void setNumBoostingIterations(int c)
c - Value to assign to numBoostingIterations.public void setMinNumInstances(int c)
c - Value to assign to minNumInstances.public int graphType()
graphType in interface Drawable
public java.lang.String graph()
throws java.lang.Exception
graph in interface Drawablejava.lang.Exception - if graph can't be computedpublic int measureTreeSize()
public int measureNumLeaves()
public java.util.Enumeration enumerateMeasures()
enumerateMeasures in interface AdditionalMeasureProducerpublic double getMeasure(java.lang.String additionalMeasureName)
getMeasure in interface AdditionalMeasureProduceradditionalMeasureName - the name of the measure to query for its value
java.lang.IllegalArgumentException - if the named measure is not supportedpublic java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic java.lang.String modelTypeTipText()
public java.lang.String binSplitTipText()
public java.lang.String errorOnProbabilitiesTipText()
public java.lang.String numBoostingIterationsTipText()
public java.lang.String minNumInstancesTipText()
public java.lang.String weightTrimBetaTipText()
public java.lang.String useAICTipText()
public java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - the commandline options
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD | |||||||||