Uses of Class
meka.core.Result

Packages that use Result

Package	Description
meka.classifiers.multilabel
meka.classifiers.multilabel.incremental
meka.classifiers.multitarget
meka.core
meka.gui.core

Uses of Result in meka.classifiers.multilabel

Methods in meka.classifiers.multilabel that return Result

Modifier and Type	Method and Description
static Result[]	Evaluation.cvModel(MultilabelClassifier h, weka.core.Instances D, int numFolds, java.lang.String top) CVModel - Split D into train/test folds, and then train and evaluate on each one.
static Result[]	Evaluation.cvModel(MultilabelClassifier h, weka.core.Instances D, int numFolds, java.lang.String top, java.lang.String vop) CVModel - Split D into train/test folds, and then train and evaluate on each one.
static Result	Evaluation.evaluateModel(MultilabelClassifier h, weka.core.Instances D_train, weka.core.Instances D_test) EvaluateModel - Build model 'h' on 'D_train', test it on 'D_test'.
static Result	Evaluation.evaluateModel(MultilabelClassifier h, weka.core.Instances D_train, weka.core.Instances D_test, java.lang.String top) EvaluateModel - Build model 'h' on 'D_train', test it on 'D_test', threshold it according to 'top', using default verbosity option.
static Result	Evaluation.evaluateModel(MultilabelClassifier h, weka.core.Instances D_train, weka.core.Instances D_test, java.lang.String top, java.lang.String vop) EvaluateModel - Build model 'h' on 'D_train', test it on 'D_test', threshold it according to 'top', verbosity 'vop'.
static Result	Evaluation.evaluateModel(MultilabelClassifier h, weka.core.Instances D_test, java.lang.String tal, java.lang.String vop) EvaluateModel - Assume 'h' is already built, test it on 'D_test', threshold it according to 'top', verbosity 'vop'.
static Result	Evaluation.testClassifier(MultilabelClassifier h, weka.core.Instances D_test) TestClassifier - test classifier h on D_test

Uses of Result in meka.classifiers.multilabel.incremental

Methods in meka.classifiers.multilabel.incremental that return Result

Modifier and Type	Method and Description
static Result[]	IncrementalEvaluation.evaluateModel(MultilabelClassifier h, weka.core.Instances D) EvaluateModel - over 20 windows.
static Result[]	IncrementalEvaluation.evaluateModel(MultilabelClassifier h, weka.core.Instances D, int numWindows, double rLabeled, java.lang.String Top, java.lang.String Vop) EvaluateModel - Evaluate a multi-label data-stream model over a moving window.
static Result	IncrementalEvaluation.evaluateModel(MultilabelClassifier h, java.lang.String[] options) EvaluateModel - Build and evaluate.

Uses of Result in meka.classifiers.multitarget

Methods in meka.classifiers.multitarget that return Result

Modifier and Type	Method and Description
Result	SCC.testClassifier(weka.classifiers.Classifier h, weka.core.Instances D_train, weka.core.Instances D_test, int[][] partition) Test classifier h, on dataset D, under super-class partition 'partition'.

Uses of Result in meka.core

Methods in meka.core that return Result

Modifier and Type	Method and Description
static Result	MLEvalUtils.averageResults(Result[] folds) AverageResults - Create a Result with the average of an array of Results by taking the average +/- standand deviation.

Methods in meka.core with parameters of type Result

Modifier and Type	Method and Description
static Result	MLEvalUtils.averageResults(Result[] folds) AverageResults - Create a Result with the average of an array of Results by taking the average +/- standand deviation.
static double[][]	StatUtils.condDepMatrix(weka.core.Instances D, Result result) CondDepMatrix - Get a Conditional Dependency Matrix.
static java.lang.String	Result.getResultAsString(Result s) GetResultAsString - print out each prediction in a Result along with its true labelset.
static java.lang.String	Result.getResultAsString(Result s, int adp) GetResultAsString - print out each prediction in a Result (to a certain number of decimal points) along with its true labelset.
static java.util.HashMap<java.lang.String,java.lang.Double>	Result.getStats(Result r, java.lang.String vop) GetStats.
static double[][]	StatUtils.LEAD(weka.core.Instances D, Result result)
static double[][]	StatUtils.LEAD(weka.core.Instances D, Result R, java.lang.String MDType) LEAD - Performs LEAD on dataset 'D', with corresponding gresult 'R', and dependency measurement type 'MDType'.
static double[][]	StatUtils.LEAD2(weka.core.Instances D, Result result) LEAD.
static void	Result.writeResultToFile(Result result, java.lang.String fname) WriteResultToFile -- write a Result 'result' out in plain text format to file 'fname'.

Uses of Result in meka.gui.core

Methods in meka.gui.core that return Result

Modifier and Type	Method and Description
Result	ResultHistory.get(int index) Returns the specified history item.
Result	ResultHistoryList.getResultAt(int index) Returns the result at the specified location.
Result	ResultHistoryList.ResultHistoryModel.getResultAt(int index) Returns the element at the specified location.
Result	ResultHistory.remove(int index) Removes the specified entry.
Result	ResultHistoryList.ResultHistoryModel.removeElementAt(int index) Removes the element at the specified location.

Methods in meka.gui.core with parameters of type Result

Modifier and Type	Method and Description
void	ResultHistory.add(Result result, java.lang.String suffix) Adds the item to the history.
void	ResultHistoryList.ResultHistoryModel.addElement(Result result, java.lang.String suffix) Adds the element to the history.
void	ResultHistoryList.addResult(Result result, java.lang.String suffix) Adds the element to the history.