Uses of Interface
weka.core.AdditionalMeasureProducer

Packages that use AdditionalMeasureProducer

weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.m5
weka.core.neighboursearch
weka.experiment

Uses of AdditionalMeasureProducer in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement AdditionalMeasureProducer

class

BayesNet Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.

Uses of AdditionalMeasureProducer in weka.classifiers.bayes.net

Classes in weka.classifiers.bayes.net that implement AdditionalMeasureProducer

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.

Uses of AdditionalMeasureProducer in weka.classifiers.functions

Classes in weka.classifiers.functions that implement AdditionalMeasureProducer

class	SimpleLogistic Classifier for building linear logistic regression models.
class	SMOreg SMOreg implements the support vector machine for regression.

Uses of AdditionalMeasureProducer in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement AdditionalMeasureProducer

class

IBk K-nearest neighbours classifier.

Uses of AdditionalMeasureProducer in weka.classifiers.meta

Classes in weka.classifiers.meta that implement AdditionalMeasureProducer

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	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).

Uses of AdditionalMeasureProducer in weka.classifiers.rules

Classes in weka.classifiers.rules that implement AdditionalMeasureProducer

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	PART Class for generating a PART decision list.
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.

Uses of AdditionalMeasureProducer in weka.classifiers.trees

Classes in weka.classifiers.trees that implement AdditionalMeasureProducer

class	ADTree Class for generating an alternating decision tree.
class	BFTree Class for building a best-first decision tree classifier.
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	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	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.

Uses of AdditionalMeasureProducer in weka.classifiers.trees.m5

Classes in weka.classifiers.trees.m5 that implement AdditionalMeasureProducer

class

M5Base M5Base.

Uses of AdditionalMeasureProducer in weka.core.neighboursearch

Classes in weka.core.neighboursearch that implement AdditionalMeasureProducer

class	BallTree Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	CoverTree Class implementing the CoverTree datastructure. The class is very much a translation of the c source code made available by the authors. For more information and original source code see: Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
class	KDTree Class implementing the KDTree search algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	LinearNNSearch Class implementing the brute force search algorithm for nearest neighbour search.
class	NearestNeighbourSearch Abstract class for nearest neighbour search.
class	PerformanceStats The class that measures the performance of a nearest neighbour search (NNS) algorithm.
class	TreePerformanceStats The class that measures the performance of a tree based nearest neighbour search algorithm.

Uses of AdditionalMeasureProducer in weka.experiment

Classes in weka.experiment that implement AdditionalMeasureProducer

class	AveragingResultProducer Takes the results from a ResultProducer and submits the average to the result listener.
class	ClassifierSplitEvaluator A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
class	CostSensitiveClassifierSplitEvaluator SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
class	CrossValidationResultProducer Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
class	DatabaseResultProducer Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
class	DensityBasedClustererSplitEvaluator A SplitEvaluator that produces results for a density based clusterer.
class	LearningRateResultProducer Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset.
class	RandomSplitResultProducer Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
class	RegressionSplitEvaluator A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.