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N

NaiveBayes class weka.classifiers.NaiveBayes.
Class for a Naive Bayes classifier using estimator classes.
NaiveBayes(). Constructor for class weka.classifiers.NaiveBayes
NaiveBayesSimple class weka.classifiers.NaiveBayesSimple.
Class for building and using a simple Naive Bayes classifier.
NaiveBayesSimple(). Constructor for class weka.classifiers.NaiveBayesSimple
name(). Method in class weka.core.Attribute
Returns the attribute's name.
name(). Method in class weka.core.Option
Returns the option's name.
NamedColor class weka.gui.treevisualizer.NamedColor.
This class contains a color name and the rgb values of that color
NamedColor(String, int, int, int). Constructor for class weka.gui.treevisualizer.NamedColor
nameTipText(). Method in class weka.filters.AttributeExpressionFilter
Returns the tip text for this property
NDConditionalEstimator class weka.estimators.NDConditionalEstimator.
Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
NDConditionalEstimator(int, double). Constructor for class weka.estimators.NDConditionalEstimator
Constructor
NeuralConnection class weka.classifiers.neural.NeuralConnection.
Abstract unit in a NeuralNetwork.
NeuralConnection(String). Constructor for class weka.classifiers.neural.NeuralConnection
Constructs The unit with the basic connection information prepared for use.
NeuralMethod interface weka.classifiers.neural.NeuralMethod.
This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.
NeuralNetwork class weka.classifiers.neural.NeuralNetwork.
A Classifier that uses backpropagation to classify instances.
NeuralNetwork(). Constructor for class weka.classifiers.neural.NeuralNetwork
The constructor.
NeuralNode class weka.classifiers.neural.NeuralNode.
This class is used to represent a node in the neuralnet.
NeuralNode(String, Random, NeuralMethod). Constructor for class weka.classifiers.neural.NeuralNode
newEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy of distribution after splitting.
newNominalRule(Attribute, Instances, int[]). Method in class weka.classifiers.OneR
Create a rule branching on this nominal attribute.
newNumericRule(Attribute, Instances, int[]). Method in class weka.classifiers.OneR
Create a rule branching on this numeric attribute
newRule(Attribute, Instances). Method in class weka.classifiers.OneR
Create a rule branching on this attribute.
next. Variable in class weka.classifiers.kstar.KStarCache.TableEntry
next table entry (separate chaining)
next(int). Method in interface weka.classifiers.IterativeClassifier
Performs one iteration.
next(int). Method in class weka.classifiers.adtree.ADTree
Performs one iteration.
nextElement(). Method in class weka.core.FastVector.FastVectorEnumeration
Returns the next element.
nextIteration(). Method in class weka.experiment.Experiment
Carries out the next iteration of the experiment.
nextIteration(). Method in class weka.experiment.RemoteExperiment
Overides the one in Experiment
nextSplitAddedOrder(). Method in class weka.classifiers.adtree.ADTree
Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
NNConditionalEstimator class weka.estimators.NNConditionalEstimator.
Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
NNConditionalEstimator(). Constructor for class weka.estimators.NNConditionalEstimator
NO_COMMAND. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Node class weka.classifiers.m5.Node.
Class for handing a node in the tree or the subtree under this node
Node class weka.gui.treevisualizer.Node.
This class records all the data about a particular node for displaying.
Node(Instances, Node). Constructor for class weka.classifiers.m5.Node
Constructs a new node
Node(Instances, Node, Options). Constructor for class weka.classifiers.m5.Node
Constructs the root of a tree
Node(String, String, int, int, Color, String). Constructor for class weka.gui.treevisualizer.Node
This will setup all the values of the node except for its top and center.
NodePlace interface weka.gui.treevisualizer.NodePlace.
This is an interface for classes that wish to take a node structure and arrange them
NOMINAL. Static variable in class weka.core.Attribute
Constant set for nominal attributes.
nominalCounts. Variable in class weka.core.AttributeStats
Counts of each nominal value
nominalLabelsTipText(). Method in class weka.filters.AddFilter
Returns the tip text for this property
NominalPrediction class weka.classifiers.evaluation.NominalPrediction.
Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
NominalPrediction(double, double[]). Constructor for class weka.classifiers.evaluation.NominalPrediction
Creates the NominalPrediction object with a default weight of 1.0.
NominalPrediction(double, double[], double). Constructor for class weka.classifiers.evaluation.NominalPrediction
Creates the NominalPrediction object.
NominalToBinaryFilter class weka.filters.NominalToBinaryFilter.
Converts all nominal attributes into binary numeric attributes.
NominalToBinaryFilter(). Constructor for class weka.filters.NominalToBinaryFilter
nominalToBinaryFilterTipText(). Method in class weka.classifiers.neural.NeuralNetwork
NONE. Static variable in class weka.gui.visualize.VisualizePanelEvent
No longer used
NonSparseToSparseFilter class weka.filters.NonSparseToSparseFilter.
A filter that converts all incoming instances into sparse format.
NonSparseToSparseFilter(). Constructor for class weka.filters.NonSparseToSparseFilter
NORM_EXPECTED_COST_NAME. Static variable in class weka.classifiers.evaluation.CostCurve
NormalEstimator class weka.estimators.NormalEstimator.
Simple probability estimator that places a single normal distribution over the observed values.
NormalEstimator(double). Constructor for class weka.estimators.NormalEstimator
Constructor that takes a precision argument.
NormalizationFilter class weka.filters.NormalizationFilter.
Normalizes all numeric values in the given dataset.
NormalizationFilter(). Constructor for class weka.filters.NormalizationFilter
normalize(). Method in class weka.classifiers.CostMatrix
Normalizes the cost matrix so that diagonal elements are zero.
normalize(double[]). Static method in class weka.core.Utils
Normalizes the doubles in the array by their sum.
normalize(double[], double). Static method in class weka.core.Utils
Normalizes the doubles in the array using the given value.
normalizeAttributesTipText(). Method in class weka.classifiers.neural.NeuralNetwork
normalizeNumericClassTipText(). Method in class weka.classifiers.neural.NeuralNetwork
normalizeTipText(). Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
normalProbability(double). Static method in class weka.core.Statistics
Returns probability that the standardized normal variate Z (mean = 0, standard deviation = 1) is less than z.
NoSplit class weka.classifiers.j48.NoSplit.
Class implementing a "no-split"-split.
NoSplit(Distribution). Constructor for class weka.classifiers.j48.NoSplit
Creates "no-split"-split for given distribution.
NullFilter class weka.filters.NullFilter.
A simple instance filter that allows no instances to pass through.
NullFilter(). Constructor for class weka.filters.NullFilter
NUM_RAND_COLS. Static variable in interface weka.classifiers.kstar.KStarConstants
numArguments(). Method in class weka.core.Option
Returns the option's number of arguments.
numAttributes(). Method in class weka.core.Instances
Returns the number of attributes.
numAttributes(). Method in class weka.core.Instance
Returns the number of attributes.
numAttributes(). Method in class weka.core.SparseInstance
Returns the number of attributes.
numBags(). Method in class weka.classifiers.j48.Distribution
Returns number of bags.
numberAttributesSelected(). Method in class weka.attributeSelection.AttributeSelection
Return the number of attributes selected from the most recent run of attribute selection
numberOfClusters(). Method in class weka.clusterers.Clusterer
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.Cobweb
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.SimpleKMeans
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.EM
Returns the number of clusters.
numberOfClusters(). Method in class weka.clusterers.DistributionMetaClusterer
Returns the number of clusters.
numberOfLinearModels(). Method in class weka.classifiers.m5.Node
Counts the number of linear models in the tree.
numClasses(). Method in class weka.classifiers.j48.Distribution
Returns number of classes.
numClasses(). Method in class weka.core.Instances
Returns the number of class labels.
numClasses(). Method in class weka.core.Instance
Returns the number of class labels.
numClustersTipText(). Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
numClustersTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
numColumns(). Method in class weka.core.Matrix
Returns the number of columns in the matrix.
numCorrect(). Method in class weka.classifiers.j48.Distribution
Returns perClass(maxClass()).
numCorrect(int). Method in class weka.classifiers.j48.Distribution
Returns perClassPerBag(index,maxClass(index)).
numDistinctValues(Attribute). Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
numDistinctValues(int). Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
NUMERIC. Static variable in class weka.core.Attribute
Constant set for numeric attributes.
NumericPrediction class weka.classifiers.evaluation.NumericPrediction.
Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
NumericPrediction(double, double). Constructor for class weka.classifiers.evaluation.NumericPrediction
Creates the NumericPrediction object with a default weight of 1.0.
NumericPrediction(double, double, double). Constructor for class weka.classifiers.evaluation.NumericPrediction
Creates the NumericPrediction object.
numericStats. Variable in class weka.core.AttributeStats
Stats on numeric value distributions
numericTipText(). Method in class weka.filters.MakeIndicatorFilter
NumericToBinaryFilter class weka.filters.NumericToBinaryFilter.
Converts all numeric attributes into binary attributes (apart from the class attribute): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
NumericToBinaryFilter(). Constructor for class weka.filters.NumericToBinaryFilter
NumericTransformFilter class weka.filters.NumericTransformFilter.
Transforms numeric attributes using a given transformation method.

Valid filter-specific options are:

-R index1,index2-index4,...
Specify list of columns to transform.

NumericTransformFilter(). Constructor for class weka.filters.NumericTransformFilter
Default constructor -- sets the default transform method to java.lang.Math.abs().
numFalseNegatives(int). Method in class weka.classifiers.Evaluation
Calculate number of false negatives with respect to a particular class.
numFalsePositives(int). Method in class weka.classifiers.Evaluation
Calculate number of false positives with respect to a particular class.
numFoldsTipText(). Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
numIncorrect(). Method in class weka.classifiers.j48.Distribution
Returns total-numCorrect().
numIncorrect(int). Method in class weka.classifiers.j48.Distribution
Returns perBag(index)-numCorrect(index).
numInstances(). Method in class weka.classifiers.Evaluation
Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances(). Method in class weka.core.Instances
Returns the number of instances in the dataset.
numLeaves(). Method in class weka.classifiers.j48.ClassifierTree
Returns number of leaves in tree structure.
numLeaves(int). Method in class weka.classifiers.m5.Node
Sets the leaves' numbers
numNeighboursTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
numNodes(). Method in class weka.classifiers.j48.ClassifierTree
Returns number of nodes in tree structure.
numOfBoostingIterationsTipText(). Method in class weka.classifiers.adtree.ADTree
numParameters(). Method in class weka.classifiers.LinearRegression
Get the number of coefficients used in the model
numPendingOutput(). Method in class weka.filters.Filter
Returns the number of instances pending output
numRows(). Method in class weka.core.Matrix
Returns the number of rows in the matrix.
numRules(). Method in class weka.classifiers.j48.MakeDecList
Outputs the number of rules in the classifier.
numRulesTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
numSubsets(). Method in class weka.classifiers.j48.ClassifierSplitModel
Returns the number of created subsets for the split.
numToSelectTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
numToSelectTipText(). Method in class weka.attributeSelection.ForwardSelection
Returns the tip text for this property
numToSelectTipText(). Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
numTrueNegatives(int). Method in class weka.classifiers.Evaluation
Calculate the number of true negatives with respect to a particular class.
numTruePositives(int). Method in class weka.classifiers.Evaluation
Calculate the number of true positives with respect to a particular class.
numValues(). Method in class weka.core.Attribute
Returns the number of attribute values.
numValues(). Method in class weka.core.Instance
Returns the number of values present.
numValues(). Method in class weka.core.SparseInstance
Returns the number of values in the sparse vector.
numXValFoldsTipText(). Method in class weka.classifiers.ThresholdSelector

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