Features are inputs and targets are outputs for penalty learning functions like penaltyLearning::IntervalRegressionCV. data(neuroblastoma, package="neuroblastoma") was processed by computing optimal Gaussian segmentation models from 1 to 20 segments (cghseg:::segmeanCO or Segmentor3IsBack::Segmentor), then label error was computed using neuroblastoma$annotations (penaltyLearning::labelError), then target intervals were computed (penaltyLearning::targetInterval). Features were also computed based on neuroblastoma$profiles.
List of two matrices: feature.mat is n.observations x n.features, and target.mat is n.observations x 2, where n.observations=3418 and n.features=117.
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