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 representColors().
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 WriterCSVResultListener().
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.