/// <summary>
/// Classification model selecting EnsembleLearner.
/// Trains several models and selects the best subset of models for the ensemble.
/// The selection of the best set of models is based on cross validation.
/// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
/// </summary>
/// <param name="learners">Learners in the ensemble</param>
/// <param name="crossValidation">Cross validation method</param>
/// <param name="ensembleStrategy">Strategy on how to combine the models</param>
/// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
public ClassificationModelSelectingEnsembleLearner(
IIndexedLearner <ProbabilityPrediction>[] learners,
ICrossValidation <ProbabilityPrediction> crossValidation,
IClassificationEnsembleStrategy ensembleStrategy,
IClassificationEnsembleSelection ensembleSelection)
: this(learners, crossValidation, () => ensembleStrategy, ensembleSelection)
{
}
/// <summary>
/// Classification model selecting EnsembleLearner.
/// Trains several models and selects the best subset of models for the ensemble.
/// The selection of the best set of models is based on cross validation.
/// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
/// </summary>
/// <param name="learners">Learners in the ensemble</param>
/// <param name="crossValidation">Cross validation method</param>
/// <param name="ensembleStrategy">Strategy on how to combine the models</param>
/// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
public ClassificationModelSelectingEnsembleLearner(
IIndexedLearner <ProbabilityPrediction>[] learners,
ICrossValidation <ProbabilityPrediction> crossValidation,
Func <IClassificationEnsembleStrategy> ensembleStrategy,
IClassificationEnsembleSelection ensembleSelection)
{
m_learners = learners ?? throw new ArgumentNullException(nameof(learners));
m_crossValidation = crossValidation ?? throw new ArgumentNullException(nameof(crossValidation));
m_ensembleStrategy = ensembleStrategy ?? throw new ArgumentNullException(nameof(ensembleStrategy));
m_ensembleSelection = ensembleSelection ?? throw new ArgumentNullException(nameof(ensembleSelection));
}
/// <summary>
/// Classification model selecting EnsembleLearner.
/// Trains several models and selects the best subset of models for the ensemble.
/// The selection of the best set of models is based on cross validation.
/// http://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
/// </summary>
/// <param name="learners">Learners in the ensemble</param>
/// <param name="crossValidation">Cross validation method</param>
/// <param name="ensembleStrategy">Strategy on how to combine the models</param>
/// <param name="ensembleSelection">Ensemble selection method used to find the beset subset of models</param>
public ClassificationModelSelectingEnsembleLearner(IIndexedLearner <ProbabilityPrediction>[] learners, ICrossValidation <ProbabilityPrediction> crossValidation,
Func <IClassificationEnsembleStrategy> ensembleStrategy, IClassificationEnsembleSelection ensembleSelection)
{
if (learners == null)
{
throw new ArgumentNullException("learners");
}
if (crossValidation == null)
{
throw new ArgumentNullException("crossValidation");
}
if (ensembleStrategy == null)
{
throw new ArgumentNullException("ensembleStrategy");
}
if (ensembleSelection == null)
{
throw new ArgumentNullException("ensembleSelection");
}
m_learners = learners;
m_crossValidation = crossValidation;
m_ensembleStrategy = ensembleStrategy;
m_ensembleSelection = ensembleSelection;
}