GitHub / Treers / MetaCost
P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.
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PURL: pkg:github/Treers/MetaCost
Stars: 37
Forks: 8
Open issues: 0
License: None
Language: Python
Size: 116 KB
Dependencies parsed at: Pending
Created at: over 6 years ago
Updated at: almost 2 years ago
Pushed at: over 6 years ago
Last synced at: almost 2 years ago
Topics: cost-sensitive, imbalanced-learning, machine-learning