Modifier and Type | Method and Description |
---|---|
Classifier |
ClassifierSubsetEval.getClassifier()
Get the classifier used as the base learner.
|
Classifier |
WrapperSubsetEval.getClassifier()
Get the classifier used as the base learner.
|
Modifier and Type | Method and Description |
---|---|
void |
ClassifierSubsetEval.setClassifier(Classifier newClassifier)
Set the classifier to use for accuracy estimation
|
void |
WrapperSubsetEval.setClassifier(Classifier newClassifier)
Set the classifier to use for accuracy estimation
|
Modifier and Type | Class and Description |
---|---|
class |
IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from a single base learner.
|
class |
MultipleClassifiersCombiner
Abstract utility class for handling settings common to
meta classifiers that build an ensemble from multiple classifiers.
|
class |
RandomizableClassifier
Abstract utility class for handling settings common to randomizable
classifiers.
|
class |
RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
RandomizableMultipleClassifiersCombiner
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from multiple classifiers based
on a given random number seed.
|
class |
RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable
meta classifiers that build an ensemble from a single base learner.
|
class |
SingleClassifierEnhancer
Abstract utility class for handling settings common to meta
classifiers that use a single base learner.
|
Modifier and Type | Method and Description |
---|---|
static Classifier |
Classifier.forName(String classifierName,
String[] options)
Creates a new instance of a classifier given it's class name and (optional)
arguments to pass to it's setOptions method.
|
Classifier |
CheckSource.getClassifier()
Gets the classifier being used for the tests, can be null.
|
Classifier |
CheckClassifier.getClassifier()
Get the classifier used as the classifier
|
Classifier |
BVDecompose.getClassifier()
Gets the name of the classifier being analysed
|
Classifier |
SingleClassifierEnhancer.getClassifier()
Get the classifier used as the base learner.
|
Classifier |
BVDecomposeSegCVSub.getClassifier()
Gets the name of the classifier being analysed
|
Classifier |
MultipleClassifiersCombiner.getClassifier(int index)
Gets a single classifier from the set of available classifiers.
|
Classifier[] |
MultipleClassifiersCombiner.getClassifiers()
Gets the list of possible classifers to choose from.
|
Classifier |
CheckSource.getSourceCode()
Gets the class to test.
|
static Classifier[] |
Classifier.makeCopies(Classifier model,
int num)
Creates a given number of deep copies of the given classifier using
serialization.
|
static Classifier |
Classifier.makeCopy(Classifier model)
Creates a deep copy of the given classifier using serialization.
|
Modifier and Type | Method and Description |
---|---|
void |
Evaluation.crossValidateModel(Classifier classifier,
Instances data,
int numFolds,
Random random,
Object... forPredictionsPrinting)
Performs a (stratified if class is nominal) cross-validation for a
classifier on a set of instances.
|
double[] |
Evaluation.evaluateModel(Classifier classifier,
Instances data,
Object... forPredictionsPrinting)
Evaluates the classifier on a given set of instances.
|
static String |
Evaluation.evaluateModel(Classifier classifier,
String[] options)
Evaluates a classifier with the options given in an array of strings.
|
double |
Evaluation.evaluateModelOnce(Classifier classifier,
Instance instance)
Evaluates the classifier on a single instance.
|
double |
Evaluation.evaluateModelOnceAndRecordPrediction(Classifier classifier,
Instance instance)
Evaluates the classifier on a single instance and records the prediction
(if the class is nominal).
|
static Classifier[] |
Classifier.makeCopies(Classifier model,
int num)
Creates a given number of deep copies of the given classifier using
serialization.
|
static Classifier |
Classifier.makeCopy(Classifier model)
Creates a deep copy of the given classifier using serialization.
|
static void |
Evaluation.printClassifications(Classifier classifier,
Instances train,
ConverterUtils.DataSource testSource,
int classIndex,
Range attributesToOutput,
boolean printDistribution,
StringBuffer text)
Prints the predictions for the given dataset into a supplied StringBuffer
|
static void |
Evaluation.printClassifications(Classifier classifier,
Instances train,
ConverterUtils.DataSource testSource,
int classIndex,
Range attributesToOutput,
StringBuffer predsText)
Prints the predictions for the given dataset into a String variable.
|
void |
CheckSource.setClassifier(Classifier value)
Sets the classifier to use for the comparison.
|
void |
CheckClassifier.setClassifier(Classifier newClassifier)
Set the classifier for boosting.
|
void |
BVDecompose.setClassifier(Classifier newClassifier)
Set the classifiers being analysed
|
void |
SingleClassifierEnhancer.setClassifier(Classifier newClassifier)
Set the base learner.
|
void |
BVDecomposeSegCVSub.setClassifier(Classifier newClassifier)
Set the classifiers being analysed
|
void |
MultipleClassifiersCombiner.setClassifiers(Classifier[] classifiers)
Sets the list of possible classifers to choose from.
|
void |
CheckSource.setSourceCode(Classifier value)
Sets the class to test.
|
Modifier and Type | Class and Description |
---|---|
class |
AODE
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
|
class |
AODEsr
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see: Fei Zheng, Geoffrey I. |
class |
BayesianLogisticRegression
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.
For more information, see Alexander Genkin, David D. |
class |
BayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
ComplementNaiveBayes
Class for building and using a Complement class Naive Bayes classifier.
For more information see, Jason D. |
class |
DMNBtext
Class for building and using a Discriminative Multinomial Naive Bayes classifier.
|
class |
HNB
Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.
For more information refer to: H. |
class |
NaiveBayes
Class for a Naive Bayes classifier using estimator classes.
|
class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesMultinomialUpdateable
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesSimple
Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.
For more information, see Richard Duda, Peter Hart (1973). |
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
|
class |
WAODE
WAODE contructs the model called Weightily Averaged One-Dependence Estimators.
For more information, see L. |
Modifier and Type | Class and Description |
---|---|
class |
BayesNetGenerator
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
BIFReader
Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.
For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998). |
class |
EditableBayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
Modifier and Type | Method and Description |
---|---|
FastVector |
EvaluationUtils.getCVPredictions(Classifier classifier,
Instances data,
int numFolds)
Generate a bunch of predictions ready for processing, by performing a
cross-validation on the supplied dataset.
|
Prediction |
EvaluationUtils.getPrediction(Classifier classifier,
Instance test)
Generate a single prediction for a test instance given the pre-trained
classifier.
|
FastVector |
EvaluationUtils.getTestPredictions(Classifier classifier,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set assuming the classifier is already trained.
|
FastVector |
EvaluationUtils.getTrainTestPredictions(Classifier classifier,
Instances train,
Instances test)
Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
|
Modifier and Type | Class and Description |
---|---|
class |
GaussianProcesses
Implements Gaussian Processes for regression without hyperparameter-tuning.
|
class |
IsotonicRegression
Learns an isotonic regression model.
|
class |
LeastMedSq
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
|
class |
LibLINEAR
A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). |
class |
LibSVM
A wrapper class for the libsvm tools (the libsvm
classes, typically the jar file, need to be in the classpath to use this
classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. |
class |
LinearRegression
Class for using linear regression for prediction.
|
class |
Logistic
Class for building and using a multinomial logistic regression model with a ridge estimator.
There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables. |
class |
MultilayerPerceptron
A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both. |
class |
PaceRegression
Class for building pace regression linear models and using them for prediction.
|
class |
PLSClassifier
A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
|
class |
RBFNetwork
Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. |
class |
SimpleLinearRegression
Learns a simple linear regression model.
|
class |
SimpleLogistic
Classifier for building linear logistic regression models.
|
class |
SMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones. |
class |
SMOreg
SMOreg implements the support vector machine for regression.
|
class |
SPegasos
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
|
class |
VotedPerceptron
Implementation of the voted perceptron algorithm by Freund and Schapire.
|
class |
Winnow
Implements Winnow and Balanced Winnow algorithms by Littlestone.
For more information, see N. |
Modifier and Type | Class and Description |
---|---|
class |
IB1
Nearest-neighbour classifier.
|
class |
IBk
K-nearest neighbours classifier.
|
class |
KStar
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
|
class |
LBR
Lazy Bayesian Rules Classifier.
|
class |
LWL
Locally weighted learning.
|
Modifier and Type | Class and Description |
---|---|
class |
AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
ClassificationViaClustering
A simple meta-classifier that uses a clusterer for classification.
|
class |
ClassificationViaRegression
Class for doing classification using regression methods.
|
class |
CostSensitiveClassifier
A metaclassifier that makes its base classifier cost-sensitive.
|
class |
CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier.
For more information, see: R. |
class |
Dagging
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
|
class |
Decorate
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
|
class |
END
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
FilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
|
class |
Grading
Implements Grading.
|
class |
GridSearch
Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.
The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). |
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
MetaCost
This metaclassifier makes its base classifier cost-sensitive using the method specified in
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. |
class |
MultiBoostAB
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. |
class |
MultiClassClassifier
A metaclassifier for handling multi-class datasets with 2-class classifiers.
|
class |
MultiScheme
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
|
class |
OrdinalClassClassifier
Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. |
class |
RacedIncrementalLogitBoost
Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. |
class |
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
|
class |
RandomSubSpace
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
|
class |
RegressionByDiscretization
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
|
class |
RotationForest
Class for construction a Rotation Forest.
|
class |
Stacking
Combines several classifiers using the stacking method.
|
class |
StackingC
Implements StackingC (more efficient version of stacking).
For more information, see A.K. |
class |
ThresholdSelector
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
|
class |
Vote
Class for combining classifiers.
|
Modifier and Type | Method and Description |
---|---|
Classifier[][] |
LogitBoost.classifiers()
Returns the array of classifiers that have been built.
|
Classifier |
GridSearch.getBestClassifier()
returns the best Classifier setup
|
Classifier |
MultiScheme.getClassifier(int index)
Gets a single classifier from the set of available classifiers.
|
Classifier[] |
MultiScheme.getClassifiers()
Gets the list of possible classifers to choose from.
|
Classifier |
Stacking.getMetaClassifier()
Gets the meta classifier.
|
Modifier and Type | Method and Description |
---|---|
void |
GridSearch.setClassifier(Classifier newClassifier)
Set the base learner.
|
void |
RacedIncrementalLogitBoost.setClassifier(Classifier newClassifier)
Set the base learner.
|
void |
MultiScheme.setClassifiers(Classifier[] classifiers)
Sets the list of possible classifers to choose from.
|
void |
Stacking.setMetaClassifier(Classifier classifier)
Adds meta classifier
|
Constructor and Description |
---|
AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument.
|
Modifier and Type | Class and Description |
---|---|
class |
ClassBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
DataNearBalancedND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
class |
ND
A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.
For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. |
Modifier and Type | Class and Description |
---|---|
class |
CitationKNN
Modified version of the Citation kNN multi instance classifier.
For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach. |
class |
MDD
Modified Diverse Density algorithm, with collective assumption.
More information about DD: Oded Maron (1998). |
class |
MIBoost
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see: Yoav Freund, Robert E. |
class |
MIDD
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998). |
class |
MIEMDD
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. |
class |
MILR
Uses either standard or collective multi-instance assumption, but within linear regression.
|
class |
MINND
Multiple-Instance Nearest Neighbour with Distribution learner.
It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0. |
class |
MIOptimalBall
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
|
class |
MISMO
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones. |
class |
MISVM
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
|
class |
MIWrapper
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see: E. |
class |
SimpleMI
Reduces MI data into mono-instance data.
|
Modifier and Type | Class and Description |
---|---|
class |
HyperPipes
Class implementing a HyperPipe classifier.
|
class |
SerializedClassifier
A wrapper around a serialized classifier model.
|
class |
VFI
Classification by voting feature intervals.
|
Modifier and Type | Method and Description |
---|---|
Classifier |
SerializedClassifier.getCurrentModel()
Gets the currently loaded model (can be null).
|
Modifier and Type | Method and Description |
---|---|
void |
SerializedClassifier.setModel(Classifier value)
Sets the fully built model to use, if one doesn't want to load a model
from a file or already deserialized a model from somewhere else.
|
Modifier and Type | Class and Description |
---|---|
class |
GeneralRegression
Class implementing import of PMML General Regression model.
|
class |
NeuralNetwork
Class implementing import of PMML Neural Network model.
|
class |
PMMLClassifier
Abstract base class for all PMML classifiers.
|
class |
Regression
Class implementing import of PMML Regression model.
|
Modifier and Type | Class and Description |
---|---|
class |
ConjunctiveRule
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. |
class |
DecisionTable
Class for building and using a simple decision table majority classifier.
For more information see: Ron Kohavi: The Power of Decision Tables. |
class |
DTNB
Class for building and using a decision table/naive bayes hybrid classifier.
|
class |
JRip
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
|
class |
M5Rules
Generates a decision list for regression problems using separate-and-conquer.
|
class |
NNge
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
|
class |
OneR
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
|
class |
PART
Class for generating a PART decision list.
|
class |
Prism
Class for building and using a PRISM rule set for classification.
|
class |
Ridor
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. |
class |
ZeroR
Class for building and using a 0-R classifier.
|
Modifier and Type | Class and Description |
---|---|
class |
ADTree
Class for generating an alternating decision tree.
|
class |
BFTree
Class for building a best-first decision tree classifier.
|
class |
DecisionStump
Class for building and using a decision stump.
|
class |
FT
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
|
class |
Id3
Class for constructing an unpruned decision tree based on the ID3 algorithm.
|
class |
J48
Class for generating a pruned or unpruned C4.5 decision tree.
|
class |
J48graft
Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
|
class |
LADTree
Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
|
class |
LMT
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
|
class |
M5P
M5Base.
|
class |
NBTree
Class for generating a decision tree with naive Bayes classifiers at the leaves.
For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. |
class |
RandomForest
Class for constructing a forest of random trees.
For more information see: Leo Breiman (2001). |
class |
RandomTree
Class for constructing a tree that considers K
randomly chosen attributes at each node.
|
class |
REPTree
Fast decision tree learner.
|
class |
SimpleCart
Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. |
class |
UserClassifier
Interactively classify through visual means.
|
Modifier and Type | Class and Description |
---|---|
class |
FTInnerNode
Class for Functional Inner tree structure.
|
class |
FTLeavesNode
Class for Functional Leaves tree version.
|
class |
FTNode
Class for Functional tree structure.
|
class |
FTtree
Abstract class for Functional tree structure.
|
Modifier and Type | Class and Description |
---|---|
class |
LMTNode
Class for logistic model tree structure.
|
class |
LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm.
|
Modifier and Type | Class and Description |
---|---|
class |
M5Base
M5Base.
|
class |
PreConstructedLinearModel
This class encapsulates a linear regression function.
|
class |
RuleNode
Constructs a node for use in an m5 tree or rule
|
Modifier and Type | Method and Description |
---|---|
Classifier |
RegressionSplitEvaluator.getClassifier()
Get the value of Classifier.
|
Classifier |
ClassifierSplitEvaluator.getClassifier()
Get the value of Classifier.
|
Modifier and Type | Method and Description |
---|---|
void |
RegressionSplitEvaluator.setClassifier(Classifier newClassifier)
Sets the classifier.
|
void |
ClassifierSplitEvaluator.setClassifier(Classifier newClassifier)
Sets the classifier.
|
Modifier and Type | Method and Description |
---|---|
Classifier |
AddClassification.getClassifier()
Gets the classifier used by the filter.
|
Modifier and Type | Method and Description |
---|---|
void |
AddClassification.setClassifier(Classifier value)
Sets the classifier to classify instances with.
|
Modifier and Type | Method and Description |
---|---|
Classifier |
RemoveMisclassified.getClassifier()
Gets the classifier used by the filter.
|
Modifier and Type | Method and Description |
---|---|
void |
RemoveMisclassified.setClassifier(Classifier classifier)
Sets the classifier to classify instances with.
|
Modifier and Type | Method and Description |
---|---|
Classifier |
Classifier.getClassifier()
Get the classifier currently set for this wrapper
|
Classifier |
BatchClassifierEvent.getClassifier()
Get the classifier
|
Classifier |
IncrementalClassifierEvent.getClassifier()
Get the classifier
|
Classifier |
Classifier.getClassifierTemplate()
Return the classifier template currently in use.
|
Modifier and Type | Method and Description |
---|---|
void |
BatchClassifierEvent.setClassifier(Classifier classifier)
Set the classifier
|
void |
IncrementalClassifierEvent.setClassifier(Classifier c) |
void |
Classifier.setClassifierTemplate(Classifier c)
Set the classifier for this wrapper
|
Constructor and Description |
---|
BatchClassifierEvent(Object source,
Classifier scheme,
DataSetEvent trsI,
DataSetEvent tstI,
int setNum,
int maxSetNum)
Creates a new
BatchClassifierEvent instance. |
BatchClassifierEvent(Object source,
Classifier scheme,
DataSetEvent trsI,
DataSetEvent tstI,
int runNum,
int maxRunNum,
int setNum,
int maxSetNum)
Creates a new
BatchClassifierEvent instance. |
IncrementalClassifierEvent(Object source,
Classifier scheme,
Instance currentI,
int status)
Creates a new
IncrementalClassifierEvent instance. |
IncrementalClassifierEvent(Object source,
Classifier scheme,
Instances structure)
Creates a new incremental classifier event that encapsulates
header information and classifier.
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Modifier and Type | Method and Description |
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static void |
BoundaryVisualizer.createNewVisualizerWindow(Classifier classifier,
Instances instances)
Creates a new GUI window with all of the BoundaryVisualizer trappings,
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void |
BoundaryVisualizer.setClassifier(Classifier newClassifier)
Set a classifier to use
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void |
RemoteBoundaryVisualizerSubTask.setClassifier(Classifier dc)
Set the classifier to use
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void |
BoundaryPanel.setClassifier(Classifier classifier)
Set the classifier to use.
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Modifier and Type | Method and Description |
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static void |
ClassifierPanel.processClassifierPrediction(Instance toPredict,
Classifier classifier,
Evaluation eval,
Instances plotInstances,
FastVector plotShape,
FastVector plotSize)
Process a classifier's prediction for an instance and update a set of
plotting instances and additional plotting info.
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