Uses of Interface
weka.classifiers.meta.multisearch.TraceableOptimizer

Packages that use TraceableOptimizer

Package	Description
weka.classifiers.meta
weka.classifiers.meta.multisearch

Uses of TraceableOptimizer in weka.classifiers.meta

Classes in weka.classifiers.meta that implement TraceableOptimizer

Modifier and Type

Class and Description

class

MultiSearch Performs a search of an arbitrary number of parameters of a classifier and chooses the best pair found for the actual filtering and training. The default MultiSearch is using the following Classifier setup: LinearRegression, searching for the "Ridge" The properties being explored are totally up to the user. E.g., if you have a FilteredClassifier selected as base classifier, sporting a PLSFilter and you want to explore the number of PLS components, then your property will be made up of the following components: - filter: referring to the FilteredClassifier's property (= PLSFilter) - numComponents: the actual property of the PLSFilter that we want to modify And assembled, the property looks like this: filter.numComponents The best classifier setup can be accessed after the buildClassifier call via the getBestClassifier method. The trace of setups evaluated can be accessed after the buildClassifier call as well, using the following methods: - getTrace() - getTraceSize() - getTraceValue(int) - getTraceFolds(int) - getTraceClassifierAsCli(int) - getTraceParameterSettings(int) Using the weka.core.setupgenerator.ParameterGroup parameter, it is possible to group dependent parameters.

Uses of TraceableOptimizer in weka.classifiers.meta.multisearch

Classes in weka.classifiers.meta.multisearch that implement TraceableOptimizer

Modifier and Type	Class and Description
class	AbstractMultiThreadedSearch Ancestor for multi-threaded searches.
class	AbstractSearch Ancestor for search algorithms.
class	DefaultSearch Performs a search of an arbitrary number of parameters of a classifier and chooses the best setup found for the actual training. The properties being explored are totally up to the user. E.g., if you have a FilteredClassifier selected as base classifier, sporting a PLSFilter and you want to explore the number of PLS components, then your property will be made up of the following components: - filter: referring to the FilteredClassifier's property (= PLSFilter) - numComponents: the actual property of the PLSFilter that we want to modify And assembled, the property looks like this: filter.numComponents The initial space is worked on with 2-fold CV to determine the values of the parameters for the selected type of evaluation (e.g., accuracy).
class	RandomSearch