public class RandomForest extends Bagging
@article{Breiman2001,
author = {Leo Breiman},
journal = {Machine Learning},
number = {1},
pages = {5-32},
title = {Random Forests},
volume = {45},
year = {2001}
}
-P Size of each bag, as a percentage of the training set size. (default 100)
-O Calculate the out of bag error.
-store-out-of-bag-predictions Whether to store out of bag predictions in internal evaluation object.
-output-out-of-bag-complexity-statistics Whether to output complexity-based statistics when out-of-bag evaluation is performed.
-print Print the individual classifiers in the output
-I <num> Number of iterations. (current value 100)
-num-slots <num> Number of execution slots. (default 1 - i.e. no parallelism) (use 0 to auto-detect number of cores)
-K <number of attributes> Number of attributes to randomly investigate. (default 0) (<1 = int(log_2(#predictors)+1)).
-M <minimum number of instances> Set minimum number of instances per leaf. (default 1)
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-S <num> Seed for random number generator. (default 1)
-depth <num> The maximum depth of the tree, 0 for unlimited. (default 0)
-N <num> Number of folds for backfitting (default 0, no backfitting).
-U Allow unclassified instances.
-B Break ties randomly when several attributes look equally good.
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
-num-decimal-places The number of decimal places for the output of numbers in the model (default 2).
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT| Constructor and Description |
|---|
RandomForest()
Constructor that sets base classifier for bagging to RandomTre and default number of iterations to 100.
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
breakTiesRandomlyTipText()
Returns the tip text for this property
|
boolean |
getBreakTiesRandomly()
Get whether to break ties randomly.
|
Capabilities |
getCapabilities()
Returns default capabilities of the base classifier.
|
int |
getMaxDepth()
Get the maximum depth of trh tree, 0 for unlimited.
|
int |
getNumFeatures()
Get the number of features used in random selection.
|
java.lang.String[] |
getOptions()
Gets the current settings of the forest.
|
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.
|
java.lang.String |
globalInfo()
Returns a string describing classifier
|
java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
|
static void |
main(java.lang.String[] argv)
Main method for this class.
|
java.lang.String |
maxDepthTipText()
Returns the tip text for this property
|
java.lang.String |
numFeaturesTipText()
Returns the tip text for this property
|
void |
setBreakTiesRandomly(boolean newBreakTiesRandomly)
Set whether to break ties randomly.
|
void |
setClassifier(Classifier newClassifier)
This method only accepts RandomTree arguments.
|
void |
setMaxDepth(int value)
Set the maximum depth of the tree, 0 for unlimited.
|
void |
setNumFeatures(int newNumFeatures)
Set the number of features to use in random selection.
|
void |
setOptions(java.lang.String[] options)
Parses a given list of options.
|
void |
setRepresentCopiesUsingWeights(boolean representUsingWeights)
This method only accepts true as its argument
|
java.lang.String |
toString()
Returns description of the bagged classifier.
|
aggregate, bagSizePercentTipText, buildClassifier, calcOutOfBagTipText, distributionForInstance, enumerateMeasures, finalizeAggregation, generatePartition, getBagSizePercent, getCalcOutOfBag, getMeasure, getMembershipValues, getOutOfBagEvaluationObject, getOutputOutOfBagComplexityStatistics, getPrintClassifiers, getRepresentCopiesUsingWeights, getStoreOutOfBagPredictions, measureOutOfBagError, numElements, outputOutOfBagComplexityStatisticsTipText, printClassifiersTipText, representCopiesUsingWeightsTipText, setBagSizePercent, setCalcOutOfBag, setOutputOutOfBagComplexityStatistics, setPrintClassifiers, setStoreOutOfBagPredictions, storeOutOfBagPredictionsTipTextgetSeed, seedTipText, setSeedgetNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlotsgetNumIterations, numIterationsTipText, setNumIterationsclassifierTipText, getClassifier, postExecution, preExecutionbatchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlacespublic RandomForest()
public Capabilities getCapabilities()
getCapabilities in interface ClassifiergetCapabilities in interface CapabilitiesHandlergetCapabilities in class SingleClassifierEnhancerCapabilitiespublic java.lang.String globalInfo()
globalInfo in class Baggingpublic TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlergetTechnicalInformation in class Bagging@ProgrammaticProperty public void setClassifier(Classifier newClassifier)
setClassifier in class SingleClassifierEnhancernewClassifier - the RandomTree to use.if - argument is not a RandomTree@ProgrammaticProperty public void setRepresentCopiesUsingWeights(boolean representUsingWeights)
setRepresentCopiesUsingWeights in class BaggingrepresentUsingWeights - must be set to true.if - argument is not truepublic java.lang.String numFeaturesTipText()
public int getNumFeatures()
public void setNumFeatures(int newNumFeatures)
newNumFeatures - Value to assign to numFeatures.public java.lang.String maxDepthTipText()
public int getMaxDepth()
public void setMaxDepth(int value)
value - the maximum depth.public java.lang.String breakTiesRandomlyTipText()
public boolean getBreakTiesRandomly()
public void setBreakTiesRandomly(boolean newBreakTiesRandomly)
newBreakTiesRandomly - true if ties are to be broken randomlypublic java.lang.String toString()
public java.util.Enumeration<Option> listOptions()
listOptions in interface OptionHandlerlistOptions in class Baggingpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class Baggingpublic void setOptions(java.lang.String[] options)
throws java.lang.Exception
-P Size of each bag, as a percentage of the training set size. (default 100)
-O Calculate the out of bag error.
-store-out-of-bag-predictions Whether to store out of bag predictions in internal evaluation object.
-output-out-of-bag-complexity-statistics Whether to output complexity-based statistics when out-of-bag evaluation is performed.
-print Print the individual classifiers in the output
-I <num> Number of iterations. (current value 100)
-num-slots <num> Number of execution slots. (default 1 - i.e. no parallelism) (use 0 to auto-detect number of cores)
-K <number of attributes> Number of attributes to randomly investigate. (default 0) (<1 = int(log_2(#predictors)+1)).
-M <minimum number of instances> Set minimum number of instances per leaf. (default 1)
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-S <num> Seed for random number generator. (default 1)
-depth <num> The maximum depth of the tree, 0 for unlimited. (default 0)
-N <num> Number of folds for backfitting (default 0, no backfitting).
-U Allow unclassified instances.
-B Break ties randomly when several attributes look equally good.
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
-num-decimal-places The number of decimal places for the output of numbers in the model (default 2).
setOptions in interface OptionHandlersetOptions in class Baggingoptions - the list of options as an array of stringsjava.lang.Exception - if an option is not supportedpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Baggingpublic static void main(java.lang.String[] argv)
argv - the options