All Packages  Class Hierarchy

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

C

C45Loader class weka.core.converters.C45Loader.
Reads C4.5 input files.
C45Loader(). Constructor for class weka.core.converters.C45Loader
C45ModelSelection class weka.classifiers.j48.C45ModelSelection.
Class for selecting a C4.5-type split for a given dataset.
C45ModelSelection(int, Instances). Constructor for class weka.classifiers.j48.C45ModelSelection
Initializes the split selection method with the given parameters.
C45PruneableClassifierTree class weka.classifiers.j48.C45PruneableClassifierTree.
Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean, boolean). Constructor for class weka.classifiers.j48.C45PruneableClassifierTree
Constructor for pruneable tree structure.
C45PruneableDecList class weka.classifiers.j48.C45PruneableDecList.
Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
C45PruneableDecList(ModelSelection, double, int). Constructor for class weka.classifiers.j48.C45PruneableDecList
Constructor for pruneable tree structure.
C45Split class weka.classifiers.j48.C45Split.
Class implementing a C4.5-type split on an attribute.
C45Split(int, int, double). Constructor for class weka.classifiers.j48.C45Split
Initializes the split model.
cacheKeyNameTipText(). Method in class weka.experiment.DatabaseResultListener
Returns the tip text for this property
calculateDerived(). Method in class weka.experiment.PairedStats
Calculates the derived statistics (significance etc).
calculateDerived(). Method in class weka.experiment.Stats
Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateStatistics(Instance, int, int, int). Method in class weka.experiment.PairedTTester
Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStdDevsTipText(). Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
CANCEL_OPTION. Static variable in class weka.gui.PropertySelectorDialog
Signifies a cancelled property selection
CANCEL_OPTION. Static variable in class weka.gui.ListSelectorDialog
Signifies a cancelled property selection
capacity(). Method in class weka.core.FastVector
Returns the capacity of the vector.
CfsSubsetEval class weka.attributeSelection.CfsSubsetEval.
CFS attribute subset evaluator.
CfsSubsetEval(). Constructor for class weka.attributeSelection.CfsSubsetEval
Constructor
check(double). Method in class weka.classifiers.j48.Distribution
Checks if at least two bags contain a minimum number of instances.
CheckClassifier class weka.classifiers.CheckClassifier.
Class for examining the capabilities and finding problems with classifiers.
CheckClassifier(). Constructor for class weka.classifiers.CheckClassifier
checkForRemainingOptions(String[]). Static method in class weka.core.Utils
Checks if the given array contains any non-empty options.
checkForStringAttributes(). Method in class weka.core.Instances
Checks for string attributes in the dataset
checkInstance(Instance). Method in class weka.core.Instances
Checks if the given instance is compatible with this dataset.
checkModel(). Method in class weka.classifiers.j48.ClassifierSplitModel
Checks if generated model is valid.
CheckOptionHandler class weka.core.CheckOptionHandler.
Simple command line checking of classes that implement OptionHandler.

Usage:

CheckOptionHandler -W optionHandlerClassName -- test options

Valid options are:

-W classname
The name of a class implementing an OptionHandler.

CheckOptionHandler(). Constructor for class weka.core.CheckOptionHandler
checkOptionHandler(OptionHandler, String[]). Static method in class weka.core.CheckOptionHandler
Runs some diagnostic tests on an optionhandler object.
checkStatus(Object). Method in class weka.experiment.RemoteEngine
Returns status information on a particular task
checkStatus(Object). Method in interface weka.experiment.Compute
Check on the status of a Task
children(). Method in class weka.classifiers.adtree.PredictionNode
Enumerates the children of this node.
chiSquared(double[][], boolean). Static method in class weka.core.ContingencyTables
Returns chi-squared probability for a given matrix.
ChiSquaredAttributeEval class weka.attributeSelection.ChiSquaredAttributeEval.
Class for Evaluating attributes individually by measuring the chi-squared statistic with respect to the class.
ChiSquaredAttributeEval(). Constructor for class weka.attributeSelection.ChiSquaredAttributeEval
Constructor
chiSquaredProbability(double, int). Static method in class weka.core.Statistics
Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean). Static method in class weka.core.ContingencyTables
Computes chi-squared statistic for a contingency table.
chooseIndex(). Method in class weka.classifiers.j48.C45PruneableDecList
Method for choosing a subset to expand.
chooseIndex(). Method in class weka.classifiers.j48.PruneableDecList
Method for choosing a subset to expand.
chooseLastIndex(). Method in class weka.classifiers.j48.C45PruneableDecList
Choose last index (ie.
chooseLastIndex(). Method in class weka.classifiers.j48.PruneableDecList
Choose last index (ie.
classAttribute(). Method in class weka.core.Instances
Returns the class attribute.
classAttribute(). Method in class weka.core.Instance
Returns class attribute.
classFirst(boolean). Method in class weka.experiment.Experiment
Sets whether the first attribute is treated as the class for all datasets involved in the experiment.
ClassificationViaRegression class weka.classifiers.ClassificationViaRegression.
Class for doing classification using regression methods.
ClassificationViaRegression(). Constructor for class weka.classifiers.ClassificationViaRegression
Classifier class weka.classifiers.Classifier.
Abstract classifier.
Classifier(). Constructor for class weka.classifiers.Classifier
ClassifierDecList class weka.classifiers.j48.ClassifierDecList.
Class for handling a rule (partial tree) for a decision list.
ClassifierDecList(ModelSelection). Constructor for class weka.classifiers.j48.ClassifierDecList
Constructor - just calls constructor of class DecList.
ClassifierPanel class weka.gui.explorer.ClassifierPanel.
This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and evaluate the classifier using a number of testing modes (test on the training data, train/test on a percentage split, n-fold cross-validation, test on a separate split).
ClassifierPanel(). Constructor for class weka.gui.explorer.ClassifierPanel
Creates the classifier panel
ClassifierSplitEvaluator class weka.experiment.ClassifierSplitEvaluator.
A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
ClassifierSplitEvaluator(). Constructor for class weka.experiment.ClassifierSplitEvaluator
No args constructor.
ClassifierSplitModel class weka.classifiers.j48.ClassifierSplitModel.
Abstract class for classification models that can be used recursively to split the data.
ClassifierSplitModel(). Constructor for class weka.classifiers.j48.ClassifierSplitModel
ClassifierSubsetEval class weka.attributeSelection.ClassifierSubsetEval.
Classifier subset evaluator.
ClassifierSubsetEval(). Constructor for class weka.attributeSelection.ClassifierSubsetEval
classifierTipText(). Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
classifierTipText(). Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
classifierTipText(). Method in class weka.classifiers.AttributeSelectedClassifier
Returns the tip text for this property
classifierTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
classifierTipText(). Method in class weka.classifiers.CostSensitiveClassifier
classifierTipText(). Method in class weka.experiment.ClassifierSplitEvaluator
Returns the tip text for this property
classifierTipText(). Method in class weka.experiment.RegressionSplitEvaluator
Returns the tip text for this property
ClassifierTree class weka.classifiers.j48.ClassifierTree.
Class for handling a tree structure used for classification.
ClassifierTree(ModelSelection). Constructor for class weka.classifiers.j48.ClassifierTree
Constructor.
CLASSIFY_CHILD. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Asks for another learning scheme to classify this node.
classifyInstance(Instance). Method in class weka.classifiers.Classifier
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.MetaCost
Classifies a given test instance.
classifyInstance(Instance). Method in class weka.classifiers.Prism
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.DistributionClassifier
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.Stacking
Classifies a given instance using the stacked classifier.
classifyInstance(Instance). Method in class weka.classifiers.CVParameterSelection
Predicts the class value for the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.OneR
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.ZeroR
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.RegressionByDiscretization
Returns a predicted class for the test instance.
classifyInstance(Instance). Method in class weka.classifiers.IB1
Classifies the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.Id3
Classifies a given test instance using the decision tree.
classifyInstance(Instance). Method in class weka.classifiers.MultiScheme
Classifies a given instance using the selected classifier.
classifyInstance(Instance). Method in class weka.classifiers.AdditiveRegression
Classify an instance.
classifyInstance(Instance). Method in class weka.classifiers.CostSensitiveClassifier
Classifies a given instance by choosing the class with the minimum expected misclassification cost.
classifyInstance(Instance). Method in class weka.classifiers.LWR
Predicts the class value for the given test instance.
classifyInstance(Instance). Method in class weka.classifiers.LinearRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierSplitModel
Classifies a given instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.J48
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierTree
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.MakeDecList
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.ClassifierDecList
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.j48.PART
Classifies an instance.
classifyInstance(Instance). Method in class weka.classifiers.m5.M5Prime
Classifies the given test instance.
classIndex(). Method in class weka.core.Instances
Returns the class attribute's index.
classIndex(). Method in class weka.core.Instance
Returns the class attribute's index.
classIsMissing(). Method in class weka.core.Instance
Tests if an instance's class is missing.
className(int). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the name of one of the classes.
ClassPanel class weka.gui.visualize.ClassPanel.
This panel displays coloured labels for nominal attributes and a spectrum for numeric attributes.
ClassPanel(). Constructor for class weka.gui.visualize.ClassPanel
classProb(int, Instance, int). Method in class weka.classifiers.j48.ClassifierSplitModel
Gets class probability for instance.
classProb(int, Instance, int). Method in class weka.classifiers.j48.BinC45Split
Gets class probability for instance.
classProb(int, Instance, int). Method in class weka.classifiers.j48.C45Split
Gets class probability for instance.
classProbLaplace(int, Instance, int). Method in class weka.classifiers.j48.ClassifierSplitModel
Gets class probability for instance.
classValue(). Method in class weka.core.Instance
Returns an instance's class value in internal format.
cleanup(). Method in class weka.classifiers.j48.BinC45ModelSelection
Sets reference to training data to null.
cleanup(). Method in class weka.classifiers.j48.C45ModelSelection
Sets reference to training data to null.
cleanup(Instances). Method in class weka.classifiers.j48.ClassifierTree
Cleanup in order to save memory.
cleanup(Instances). Method in class weka.classifiers.j48.ClassifierDecList
Cleanup in order to save memory.
clear(). Method in class weka.classifiers.kstar.KStarCache.CacheTable
Clears this hashtable so that it contains no keys.
clear(). Method in class weka.classifiers.kstar.LightHashTable
Clears this hashtable so that it contains no keys.
clone(). Method in interface weka.classifiers.IterativeClassifier
Performs a deep copy of the classifier, and a reference copy of the training instances (or a deep copy if required).
clone(). Method in class weka.classifiers.adtree.Splitter
Clones this node.
clone(). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Clones this node.
clone(). Method in class weka.classifiers.adtree.ADTree
Creates a clone that is identical to the current tree, but is independent.
clone(). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Clones this node.
clone(). Method in class weka.classifiers.adtree.PredictionNode
Clones this node.
clone(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Creates and returns a clone of this object.
clone(). Method in class weka.classifiers.j48.ClassifierSplitModel
Allows to clone a model (shallow copy).
clone(). Method in class weka.classifiers.j48.Distribution
Clones distribution (Deep copy of distribution).
clone(). Method in class weka.core.Matrix
Creates and returns a clone of this object.
Clusterer class weka.clusterers.Clusterer.
Abstract clusterer.
Clusterer(). Constructor for class weka.clusterers.Clusterer
ClustererPanel class weka.gui.explorer.ClustererPanel.
This panel allows the user to select and configure a clusterer, and evaluate the clusterer using a number of testing modes (test on the training data, train/test on a percentage split, test on a separate split).
ClustererPanel(). Constructor for class weka.gui.explorer.ClustererPanel
Creates the clusterer panel
ClusterEvaluation class weka.clusterers.ClusterEvaluation.
Class for evaluating clustering models.

Valid options are:

-t
Specify the training file.

ClusterEvaluation(). Constructor for class weka.clusterers.ClusterEvaluation
Constructor.
clusterInstance(Instance). Method in class weka.clusterers.Clusterer
Classifies a given instance.
clusterInstance(Instance). Method in class weka.clusterers.Cobweb
Classifies a given instance.
clusterInstance(Instance). Method in class weka.clusterers.SimpleKMeans
Classifies a given instance.
clusterInstance(Instance). Method in class weka.clusterers.DistributionClusterer
Assigns an instance to a Cluster.
clusterResultsToString(). Method in class weka.clusterers.ClusterEvaluation
return the results of clustering.
Cobweb class weka.clusterers.Cobweb.
Class implementing the Cobweb and Classit clustering algorithms.

Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.

Cobweb(). Constructor for class weka.clusterers.Cobweb
cochransCriterion(double[][]). Static method in class weka.core.ContingencyTables
Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns coding costs of model.
codingCost(). Method in class weka.classifiers.j48.C45Split
Returns coding cost for split (used in rule learner).
collapse(). Method in class weka.classifiers.j48.C45PruneableClassifierTree
Collapses a tree to a node if training error doesn't increase.
Colors class weka.gui.treevisualizer.Colors.
This class maintains a list that contains all the colornames from the dotty standard and what color (in RGB) they represent
Colors(). Constructor for class weka.gui.treevisualizer.Colors
combine(Function, Function). Static method in class weka.classifiers.m5.Function
Constructs a new function of which the variable list is a combination of those of two functions
combine(int[], int[]). Static method in class weka.classifiers.m5.Ivector
Outputs a new integer vector which contains all the values in two integer vectors; assuming list1 and list2 are incrementally sorted and no identical integers within each integer vector
compactify(). Method in class weka.core.Instances
Compactifies the set of instances.
compareOptions(String[], String[]). Static method in class weka.core.CheckOptionHandler
Compares the two given sets of options.
comparisonString(int, Instances). Method in class weka.classifiers.adtree.Splitter
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances). Method in class weka.classifiers.adtree.TwoWayNumericSplit
Gets the string describing the comparision the split depends on for a particular branch.
Compute interface weka.experiment.Compute.
Interface to something that can accept remote connections and execute a task.
ConditionalEstimator interface weka.estimators.ConditionalEstimator.
Interface for conditional probability estimators.
confidenceForRule(ItemSet, ItemSet). Static method in class weka.associations.ItemSet
Outputs the confidence for a rule.
ConfusionMatrix class weka.classifiers.evaluation.ConfusionMatrix.
Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
confusionMatrix(). Method in class weka.classifiers.Evaluation
Returns a copy of the confusion matrix.
ConfusionMatrix(String[]). Constructor for class weka.classifiers.evaluation.ConfusionMatrix
Creates the confusion matrix with the given class names.
connect(NeuralConnection, NeuralConnection). Static method in class weka.classifiers.neural.NeuralConnection
Connects two units together.
CONNECTED. Static variable in class weka.classifiers.neural.NeuralConnection
This flag is set once the unit has a connection.
connectToDatabase(). Method in class weka.experiment.DatabaseUtils
Opens a connection to the database
ConsistencySubsetEval class weka.attributeSelection.ConsistencySubsetEval.
Consistency attribute subset evaluator.
ConsistencySubsetEval.hashKey class weka.attributeSelection.ConsistencySubsetEval.hashKey.
Class providing keys to the hash table.
ConsistencySubsetEval.hashKey(ConsistencySubsetEval, double[]). Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey
Constructor for a hashKey
ConsistencySubsetEval.hashKey(ConsistencySubsetEval, Instance, int). Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey
Constructor for a hashKey
ConsistencySubsetEval(). Constructor for class weka.attributeSelection.ConsistencySubsetEval
Constructor.
CONST_AUTOMATIC_SHAPE. Static variable in class weka.gui.visualize.Plot2D
containedBy(Instance). Method in class weka.associations.ItemSet
Checks if an instance contains an item set.
containsKey(double). Method in class weka.classifiers.kstar.KStarCache
Checks if the specified key maps with an entry in the cache table
containsKey(double). Method in class weka.classifiers.kstar.KStarCache.CacheTable
Tests if the specified double is a key in this hashtable.
containsKey(double). Method in class weka.classifiers.kstar.LightHashTable
Tests if the specified double is a key in this hashtable.
ContingencyTables class weka.core.ContingencyTables.
Class implementing some statistical routines for contingency tables.
ContingencyTables(). Constructor for class weka.core.ContingencyTables
ConverterUtils class weka.core.converters.ConverterUtils.
Utility routines for the converter package.
ConverterUtils(). Constructor for class weka.core.converters.ConverterUtils
convertInstance(Instance). Method in interface weka.attributeSelection.AttributeTransformer
Transforms an instance in the format of the original data to the transformed space
convertInstance(Instance). Method in class weka.attributeSelection.PrincipalComponents
Transform an instance in original (unormalized) format.
convertNewLines(String). Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
convertToAttribX(double). Method in class weka.gui.visualize.Plot2D
convert a Panel x coordinate to a raw x value.
convertToAttribY(double). Method in class weka.gui.visualize.Plot2D
convert a Panel y coordinate to a raw y value.
convertToPanelX(double). Method in class weka.gui.visualize.Plot2D
Convert an raw x value to Panel x coordinate.
convertToPanelY(double). Method in class weka.gui.visualize.Plot2D
Convert an raw y value to Panel y coordinate.
convictionForRule(ItemSet, ItemSet, int, int). Method in class weka.associations.ItemSet
Outputs the conviction for a rule.
copy(). Method in class weka.classifiers.m5.Function
Makes a copy of a function
copy(). Method in class weka.classifiers.m5.Errors
Makes a copy of the Errors object
copy(). Method in class weka.classifiers.m5.SplitInfo
Makes a copy of this SplitInfo object
copy(). Method in class weka.core.FastVector
Produces a shallow copy of this vector.
copy(). Method in class weka.core.Attribute
Produces a shallow copy of this attribute.
copy(). Method in class weka.core.Instance
Produces a shallow copy of this instance.
copy(). Method in class weka.core.SparseInstance
Produces a shallow copy of this instance.
copy(). Method in class weka.core.BinarySparseInstance
Produces a shallow copy of this instance.
copy(). Method in interface weka.core.Copyable
This method produces a shallow copy of an object.
copy(double[], int). Static method in class weka.classifiers.m5.Dvector
Returns a copy of the first n elements of a double vector
copy(int[], int). Static method in class weka.classifiers.m5.Ivector
Makes a copy of the first n elements in an integer vector
copy(Node). Method in class weka.classifiers.m5.Node
Makes a copy of the tree under this node
Copyable interface weka.core.Copyable.
Interface implemented by classes that can produce "shallow" copies of their objects.
CopyAttributesFilter class weka.filters.CopyAttributesFilter.
An instance filter that copies a range of attributes in the dataset.
CopyAttributesFilter(). Constructor for class weka.filters.CopyAttributesFilter
copyElements(). Method in class weka.core.FastVector
Clones the vector and shallow copies all its elements.
correct(). Method in class weka.classifiers.Evaluation
Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correct(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of correct classifications (that is, for which a correct prediction was made).
correlation. Variable in class weka.experiment.PairedStats
The correlation coefficient
correlation(double[], double[], int). Static method in class weka.classifiers.m5.M5Utils
Returns the correlation coefficient of two double vectors
correlation(double[], double[], int). Static method in class weka.core.Utils
Returns the correlation coefficient of two double vectors.
correlationCoefficient(). Method in class weka.classifiers.Evaluation
Returns the correlation coefficient if the class is numeric.
CostCurve class weka.classifiers.evaluation.CostCurve.
Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
CostCurve(). Constructor for class weka.classifiers.evaluation.CostCurve
CostMatrix class weka.classifiers.CostMatrix.
Class for a misclassification cost matrix.
CostMatrix(CostMatrix). Constructor for class weka.classifiers.CostMatrix
Creates a cost matrix identical to an existing matrix.
CostMatrix(int). Constructor for class weka.classifiers.CostMatrix
Creates a default cost matrix for the given number of classes.
CostMatrix(Reader). Constructor for class weka.classifiers.CostMatrix
Creates a cost matrix from a cost file.
CostMatrixEditor class weka.gui.CostMatrixEditor.
A PropertyEditor for CostMatrices.
CostMatrixEditor(). Constructor for class weka.gui.CostMatrixEditor
costMatrixSourceTipText(). Method in class weka.classifiers.CostSensitiveClassifier
costMatrixTipText(). Method in class weka.classifiers.CostSensitiveClassifier
CostSensitiveClassifier class weka.classifiers.CostSensitiveClassifier.
This metaclassifier makes its base classifier cost-sensitive.
CostSensitiveClassifier(). Constructor for class weka.classifiers.CostSensitiveClassifier
CostSensitiveClassifierSplitEvaluator class weka.experiment.CostSensitiveClassifierSplitEvaluator.
A SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveClassifierSplitEvaluator(). Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
count. Variable in class weka.experiment.PairedStats
The number of data points seen
count. Variable in class weka.experiment.Stats
The number of values seen
CramersV(double[][]). Static method in class weka.core.ContingencyTables
Computes Cramer's V for a contingency table.
create(Reader). Method in class weka.gui.treevisualizer.TreeBuild
This will build A node structure from the dotty format passed.
createExperimentIndex(). Method in class weka.experiment.DatabaseUtils
Attempts to create the experiment index table
createExperimentIndexEntry(ResultProducer). Method in class weka.experiment.DatabaseUtils
Attempts to insert a results entry for the table into the experiment index.
createResultsTable(ResultProducer, String). Method in class weka.experiment.DatabaseUtils
Creates a results table for the supplied result producer.
crossoverProbTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
CrossValidateAttributes(). Method in class weka.attributeSelection.AttributeSelection
Perform a cross validation for attribute selection.
crossValidateModel(Classifier, Instances, int). Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[]). Method in class weka.classifiers.Evaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[]). Static method in class weka.clusterers.ClusterEvaluation
Performs a cross-validation for a distribution clusterer on a set of instances.
CrossValidationResultProducer class weka.experiment.CrossValidationResultProducer.
Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
CrossValidationResultProducer(). Constructor for class weka.experiment.CrossValidationResultProducer
CSVLoader class weka.core.converters.CSVLoader.
Reads a text file that is comma or tab delimited..
CSVLoader(). Constructor for class weka.core.converters.CSVLoader
CSVResultListener class weka.experiment.CSVResultListener.
CSVResultListener outputs the received results in csv format to a Writer
CSVResultListener(). Constructor for class weka.experiment.CSVResultListener
cutoffTipText(). Method in class weka.clusterers.Cobweb
Returns the tip text for this property
CVParameterSelection class weka.classifiers.CVParameterSelection.
Class for performing parameter selection by cross-validation for any classifier.
CVParameterSelection(). Constructor for class weka.classifiers.CVParameterSelection
CVResultsString(). Method in class weka.attributeSelection.AttributeSelection
returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Packages  Class Hierarchy