EnsembleSelection

algorithm

the algorithm used to optimizer the ensemble

debug

If set to true, classifier may output additional info to the console.

greedySortInitialization

Whether sort initialization greedily stops adding models when performance degrades.

hillclimbIterations

The number of hillclimbing iterations for the ensemble selection algorithm.

hillclimbMetric

the metric that will be used to optimizer the chosen ensemble..

library

An ensemble library.

modelRatio

The ratio of library models that will be randomly chosen to be used for each iteration.

numFolds

The number of folds used for cross-validation.

numModelBags

The number of "model bags" used in the ensemble selection algorithm.

replacement

Whether models in the library can be included more than once in an ensemble.

seed

The random number seed to be used.

sortInitializationRatio

The ratio of library models to be used for sort initialization.

validationRatio

The ratio of the training data set that will be reserved for validation.

verboseOutput

Whether metrics are printed for each model.

workingDirectory

The working directory of the ensemble - where trained models will be stored.

Class

Nominal class, Numeric class, Binary class

Attributes

Unary attributes, Empty nominal attributes, Nominal attributes, Date attributes, Missing values, Numeric attributes, Binary attributes

Min # of instances

1