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Uses of OptionHandler in weka.associations |
Classes in weka.associations that implement OptionHandler | |
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Apriori
Class implementing an Apriori-type algorithm. |
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Tertius
Class implementing a Tertius-type algorithm. |
Uses of OptionHandler in weka.attributeSelection |
Classes in weka.attributeSelection that implement OptionHandler | |
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BestFirst
Class for performing a best first search. |
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CfsSubsetEval
CFS attribute subset evaluator. |
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ChiSquaredAttributeEval
Class for Evaluating attributes individually by measuring the chi-squared statistic with respect to the class. |
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ClassifierSubsetEval
Classifier subset evaluator. |
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ExhaustiveSearch
Class for performing an exhaustive search. |
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ForwardSelection
Class for performing a forward selection hill climbing search. |
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GainRatioAttributeEval
Class for Evaluating attributes individually by measuring gain ratio with respect to the class. |
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GeneticSearch
Class for performing a genetic based search. |
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InfoGainAttributeEval
Class for Evaluating attributes individually by measuring information gain with respect to the class. |
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OneRAttributeEval
Class for Evaluating attributes individually by using the OneR classifier. |
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PrincipalComponents
Class for performing principal components analysis/transformation. |
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RaceSearch
Class for performing a racing search. |
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RandomSearch
Class for performing a random search. |
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Ranker
Class for ranking the attributes evaluated by a AttributeEvaluator Valid options are: -P Specify a starting set of attributes. |
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RankSearch
Class for evaluating a attribute ranking (given by a specified evaluator) using a specified subset evaluator. |
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ReliefFAttributeEval
Class for Evaluating attributes individually using ReliefF. |
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SVMAttributeEval
Class for Evaluating attributes individually by using the SVM classifier. |
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SymmetricalUncertAttributeEval
Class for Evaluating attributes individually by measuring symmetrical uncertainty with respect to the class. |
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WrapperSubsetEval
Wrapper attribute subset evaluator. |
Uses of OptionHandler in weka.classifiers |
Classes in weka.classifiers that implement OptionHandler | |
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BVDecompose
Class for performing a Bias-Variance decomposition on any classifier using the method specified in: R. |
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BVDecomposeSegCVSub
This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in: Geoffrey I. |
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CheckClassifier
Class for examining the capabilities and finding problems with classifiers. |
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Classifier
Abstract classifier. |
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IteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner. |
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MultipleClassifiersCombiner
Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers. |
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RandomizableClassifier
Abstract utility class for handling settings common to randomizable classifiers. |
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RandomizableIteratedSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner. |
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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. |
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RandomizableSingleClassifierEnhancer
Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner. |
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SingleClassifierEnhancer
Abstract utility class for handling settings common to meta classifiers that use a single base learner. |
Uses of OptionHandler in weka.classifiers.bayes |
Classes in weka.classifiers.bayes that implement OptionHandler | |
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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. |
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BayesNet
Base class for a Bayes Network classifier. |
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BayesNetB
Class for a Bayes Network classifier based on a hill climbing algorithm for learning structure as described in Buntine, W. (1991). |
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BayesNetB2
Class for a Bayes Network classifier based on Buntines hill climbing algorithm for learning structure, but augmented to allow arc reversal as an operation. |
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BayesNetK2
Class for a Bayes Network classifier based on K2 for learning structure. |
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ComplementNaiveBayes
Class for building and using a Complement class Naive Bayes classifier. |
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NaiveBayes
Class for a Naive Bayes classifier using estimator classes. |
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NaiveBayesMultinomial
The core equation for this classifier: P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule) where Ci is class i and D is a document |
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NaiveBayesSimple
Class for building and using a simple Naive Bayes classifier. |
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NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes. |
Uses of OptionHandler in weka.classifiers.functions |
Classes in weka.classifiers.functions that implement OptionHandler | |
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LeastMedSq
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions. |
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LinearRegression
Class for using linear regression for prediction. |
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Logistic
Second implementation for building and using a multinomial logistic regression model with a ridge estimator. |
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MultilayerPerceptron
A Classifier that uses backpropagation to classify instances. |
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PaceRegression
Class for building pace regression linear models and using them for prediction. |
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RBFNetwork
Class that implements a radial basis function network. |
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SimpleLinearRegression
Class for learning a simple linear regression model. |
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SimpleLogistic
Class for building a logistic regression model using LogitBoost. |
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SMO
Implements John C. |
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SMOreg
Implements Alex J.Smola and Bernhard Scholkopf sequential minimal optimization algorithm for training a support vector regression using polynomial or RBF kernels. |
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VotedPerceptron
Implements the voted perceptron algorithm by Freund and Schapire. |
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Winnow
Implements Winnow and Balanced Winnow algorithms by N. |
Uses of OptionHandler in weka.classifiers.lazy |
Classes in weka.classifiers.lazy that implement OptionHandler | |
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IB1
IB1-type classifier. |
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IBk
K-nearest neighbours classifier. |
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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. |
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LBR
Lazy Bayesian Rules implement a lazy learning approach to lessening the attribute-independence assumption of naive Bayes. |
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LWL
Locally-weighted learning. |
Uses of OptionHandler in weka.classifiers.meta |
Classes in weka.classifiers.meta that implement OptionHandler | |
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AdaBoostM1
Class for boosting a classifier using Freund & Schapire's Adaboost M1 method. |
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AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier. |
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AttributeSelectedClassifier
Class for running an arbitrary classifier on data that has been reduced through attribute selection. |
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Bagging
Class for bagging a classifier. |
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ClassificationViaRegression
Class for doing classification using regression methods. |
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CostSensitiveClassifier
This metaclassifier makes its base classifier cost-sensitive. |
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CVParameterSelection
Class for performing parameter selection by cross-validation for any classifier. |
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Decorate
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. |
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END
Class for creating a committee of random classifiers. |
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FilteredClassifier
Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. |
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Grading
Implements Grading. |
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HND
Class to create levelwise NDs with respect to a given hierarchy of classes. |
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LogitBoost
Class for performing additive logistic regression.. |
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MetaCost
This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos (1999). |
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MultiBoostAB
Class for boosting a classifier using the MultiBoosting method. |
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MultiClassClassifier
Class for handling multi-class datasets with 2-class distribution classifiers. |
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MultiScheme
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. |
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ND
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OrdinalClassClassifier
Meta classifier for transforming an ordinal class problem to a series of binary class problems. |
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RacedIncrementalLogitBoost
Classifier for incremental learning of large datasets by way of racing logit-boosted committees. |
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RandomCommittee
Class for creating a committee of random classifiers. |
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RegressionByDiscretization
Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute (equal-width) discretized. |
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Stacking
Implements stacking. |
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StackingC
Implements StackingC (more efficient version of stacking). |
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ThresholdSelector
Class for selecting a threshold on a probability output by a distribution classifier. |
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TreeBasedMultiClassClassifier
Class that represents and builds a classifier tree. |
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Vote
Class for combining classifiers using unweighted average of probability estimates (classification) or numeric predictions (regression). |
Uses of OptionHandler in weka.classifiers.misc |
Classes in weka.classifiers.misc that implement OptionHandler | |
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FLR
Fuzzy Lattice Reasoning Classifier FLR Classifier implementation in WEKA The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. |
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HyperPipes
Class implementing a HyperPipe classifier. |
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VFI
Class implementing the voting feature interval classifier. |
Uses of OptionHandler in weka.classifiers.rules |
Classes in weka.classifiers.rules that implement OptionHandler | |
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ConjunctiveRule
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. |
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DecisionTable
Class for building and using a simple decision table majority classifier. |
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JRip
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which is proposed by William W. |
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M5Rules
Generates a decision list for regression problems using separate-and-conquer. |
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NNge
NNge classifier. |
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OneR
Class for building and using a 1R classifier. |
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PART
Class for generating a PART decision list. |
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Prism
Class for building and using a PRISM rule set for classifcation. |
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Ridor
The implementation of a RIpple-DOwn Rule learner. |
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ZeroR
Class for building and using a 0-R classifier. |
Uses of OptionHandler in weka.classifiers.trees |
Classes in weka.classifiers.trees that implement OptionHandler | |
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ADTree
Class for generating an alternating decision tree. |
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DecisionStump
Class for building and using a decision stump. |
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Id3
Class implementing an Id3 decision tree classifier. |
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J48
Class for generating an unpruned or a pruned C4.5 decision tree. |
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LMT
Class for "logistic model tree" classifier. |
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M5P
M5P. |
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RandomForest
Class for constructing random forests. |
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RandomTree
Class for constructing a tree that considers K random features at each node. |
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REPTree
Fast decision tree learner. |
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UserClassifier
Class for generating an user defined decision tree. |
Uses of OptionHandler in weka.classifiers.trees.lmt |
Classes in weka.classifiers.trees.lmt that implement OptionHandler | |
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LMTNode
Class for logistic model tree structure. |
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LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm. |
Uses of OptionHandler in weka.classifiers.trees.m5 |
Classes in weka.classifiers.trees.m5 that implement OptionHandler | |
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M5Base
M5Base. |
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PreConstructedLinearModel
This class encapsulates a linear regression function. |
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RuleNode
Constructs a node for use in an m5 tree or rule |
Uses of OptionHandler in weka.clusterers |
Classes in weka.clusterers that implement OptionHandler | |
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Cobweb
Class implementing the Cobweb and Classit clustering algorithms. |
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EM
Simple EM (expectation maximisation) class. |
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FarthestFirst
Implements the "Farthest First Traversal Algorithm" by Hochbaum and Shmoys 1985: A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10(2):180-184, as cited by Sanjoy Dasgupta "performance guarantees for hierarchical clustering", colt 2002, sydney works as a fast simple approximate clusterer modelled after SimpleKMeans, might be a useful initializer for it Valid options are: -N Specify the number of clusters to generate. |
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MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a distribution and density. |
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SimpleKMeans
Simple k means clustering class. |
Uses of OptionHandler in weka.core |
Methods in weka.core with parameters of type OptionHandler | |
static void |
CheckOptionHandler.checkOptionHandler(OptionHandler oh,
java.lang.String[] options)
Runs some diagnostic tests on an optionhandler object. |
Uses of OptionHandler in weka.datagenerators |
Classes in weka.datagenerators that implement OptionHandler | |
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BIRCHCluster
Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters. |
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RDG1
Class to generate data randomly by producing a decision list. |
Uses of OptionHandler in weka.experiment |
Classes in weka.experiment that implement OptionHandler | |
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AveragingResultProducer
AveragingResultProducer takes the results from a ResultProducer and submits the average to the result listener. |
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ClassifierSplitEvaluator
A SplitEvaluator that produces results for a classification scheme on a nominal class attribute. |
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CostSensitiveClassifierSplitEvaluator
A SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs. |
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CrossValidationResultProducer
Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results. |
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CSVResultListener
CSVResultListener outputs the received results in csv format to a Writer |
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DatabaseResultProducer
DatabaseResultProducer examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener. |
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Experiment
Holds all the necessary configuration information for a standard type experiment. |
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InstanceQuery
Convert the results of a database query into instances. |
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InstancesResultListener
InstancesResultListener outputs the received results in arff format to a Writer. |
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LearningRateResultProducer
LearningRateResultProducer takes the results from a ResultProducer and submits the average to the result listener. |
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PairedCorrectedTTester
Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic. |
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PairedTTester
Calculates T-Test statistics on data stored in a set of instances. |
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RandomSplitResultProducer
Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results. |
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RegressionSplitEvaluator
A SplitEvaluator that produces results for a classification scheme on a numeric class attribute. |
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RemoteExperiment
Holds all the necessary configuration information for a distributed experiment. |
Uses of OptionHandler in weka.filters.supervised.attribute |
Classes in weka.filters.supervised.attribute that implement OptionHandler | |
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AttributeSelection
Filter for doing attribute selection. |
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ClassOrder
A filter that sorts the order of classes so that the class values are no longer of in the order of that in the header file after filtered. |
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Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. |
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NominalToBinary
Converts all nominal attributes into binary numeric attributes. |
Uses of OptionHandler in weka.filters.supervised.instance |
Classes in weka.filters.supervised.instance that implement OptionHandler | |
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Resample
Produces a random subsample of a dataset. |
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SpreadSubsample
Produces a random subsample of a dataset. |
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StratifiedRemoveFolds
This filter takes a dataset and outputs folds suitable for cross validation. |
Uses of OptionHandler in weka.filters.unsupervised.attribute |
Classes in weka.filters.unsupervised.attribute that implement OptionHandler | |
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AbstractTimeSeries
An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance. |
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Add
An instance filter that adds a new attribute to the dataset. |
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AddCluster
A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm. |
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AddExpression
Applys a mathematical expression involving attributes and numeric constants to a dataset. |
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AddNoise
Introduces noise data a random subsample of the dataset by changing a given attribute (attribute must be nominal) Valid options are: -C col Index of the attribute to be changed. |
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ClusterMembership
A filter that uses a clusterer to obtain cluster membership probabilites for each input instance and outputs them as new instances. |
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Copy
An instance filter that copies a range of attributes in the dataset. |
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FirstOrder
This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance. eg: Original attribute values 0.1, 0.2, 0.3, 0.1, 0.3
New attribute values 0.1, 0.1, 0.1, -0.2, -0.2
The range of attributes used is taken in numeric order. |
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MakeIndicator
Creates a new dataset with a boolean attribute replacing a nominal attribute. |
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MergeTwoValues
Merges two values of a nominal attribute. |
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NumericTransform
Transforms numeric attributes using a given transformation method. |
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PKIDiscretize
Discretizes numeric attributes using equal frequency binning where the number of bins is equal to the square root of the number of non-missing values. |
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RandomProjection
Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (It will reduce the number of attributes in the data while preserving much of its variation like PCA, but at a much less computational cost). |
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Remove
An instance filter that deletes a range of attributes from the dataset. |
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RemoveType
A filter that removes attributes of a given type. |
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RemoveUseless
This filter removes attributes that do not vary at all or that vary too much. |
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StringToNominal
Converts a string attribute (i.e. unspecified number of values) to nominal (i.e. set number of values). |
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StringToWordVector
Converts String attributes into a set of attributes representing word occurrence information from the text contained in the strings. |
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SwapValues
Swaps two values of a nominal attribute. |
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TimeSeriesDelta
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance. |
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TimeSeriesTranslate
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute attribute values of some previous (or future) instance. |
Uses of OptionHandler in weka.filters.unsupervised.instance |
Classes in weka.filters.unsupervised.instance that implement OptionHandler | |
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Randomize
This filter randomly shuffles the order of instances passed through it. |
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RemoveFolds
This filter takes a dataset and outputs a specified fold for cross validation. |
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RemoveMisclassified
A filter that removes instances which are incorrectly classified. |
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RemovePercentage
This filter removes a given percentage of a dataset. |
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RemoveRange
This filter takes a dataset and removes a subset of it. |
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RemoveWithValues
Filters instances according to the value of an attribute. |
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