Create a baseline model for multilabel classification.
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A mldr dataset used to train the binary models.
Define the strategy used to predict the labels.
The possible values are:
Baseline is a naive multi-label classifier that maximize/minimize a specific measure without induces a learning model. It uses the general information about the labels in training dataset to estimate the labels in a test dataset.
The follow strategies are available:
Predict the k most frequent labels, where k is the integer most close of label cardinality.
Predict the most frequent labels that obtain the best F1 measure in training data. In the original paper, the authors use the less frequent labels.
Predict the labels that are associated with more than 50% of instances.
Predict the most common labelset.
Predict a ranking based on the most frequent labels.
An object of class
BASELINEmodel containing the set of fitted
A vector with the label names.
A list with the labels that will be predicted.
Metz, J., Abreu, L. F. de, Cherman, E. A., & Monard, M. C. (2012). On the Estimation of Predictive Evaluation Measure Baselines for Multi-label Learning. In 13th Ibero-American Conference on AI (pp. 189-198). Cartagena de Indias, Colombia.
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