Uses of Interface
edu.illinois.cs.cogcomp.lbjava.learn.TestingMetric

Packages that use TestingMetric

Package	Description
edu.illinois.cs.cogcomp.lbjava
edu.illinois.cs.cogcomp.lbjava.learn

Uses of TestingMetric in edu.illinois.cs.cogcomp.lbjava

Fields in edu.illinois.cs.cogcomp.lbjava declared as TestingMetric

Modifier and Type	Field and Description
protected TestingMetric	Train.TrainingThread.testingMetric The metric with which to measure the learner's performance on a test set.

Uses of TestingMetric in edu.illinois.cs.cogcomp.lbjava.learn

Classes in edu.illinois.cs.cogcomp.lbjava.learn that implement TestingMetric

Modifier and Type	Class and Description
class	Accuracy Returns the accuracy of a discrete classifier with respect to the oracle as the fraction of examples for which its prediction was correct.

Methods in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type TestingMetric

Modifier and Type	Method and Description
double[][]	BatchTrainer.crossValidation(int[] rounds, int k, FoldParser.SplitPolicy splitPolicy, double alpha, TestingMetric metric, boolean statusMessages) Performs cross validation, computing a confidence interval on the performance of the learner after each of the specified rounds of training.
protected double	BatchTrainer.crossValidationTesting(FoldParser foldParser, TestingMetric metric, boolean clone, boolean statusMessages) Tests the learner as a subroutine inside cross validation.
protected double	BatchTrainer.testMidTraining(Parser testParser, TestingMetric metric, boolean clone) Tests BatchTrainer.learner on the specified data while making provisions under the assumption that this test happens in between rounds of training.
Learner.Parameters	BatchTrainer.tune(Learner.Parameters[] parameters, int[] rounds, int k, FoldParser.SplitPolicy splitPolicy, double alpha, TestingMetric metric) Tune learning algorithm parameters using cross validation.
Learner.Parameters	BatchTrainer.tune(Learner.Parameters[] parameters, int[] rounds, Parser devParser, TestingMetric metric) Tune learning algorithm parameters against a development set.