About: Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data. Changes:Updated license information
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About: A python implementation of Breiman's Random Forests. Changes:Initial Announcement on mloss.org.
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About: The open source Error-Correcting Output Codes (ECOC) library contains both state-of-the-art coding and decoding designs, as well as the option to include your own coding, decoding, and base classifier. Changes:Initial Announcement on mloss.org.
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