Multi-label classification
- Multi-label classification
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In machine learning, multi-label classification is a variant of the classification problem where multiple target labels must be assigned to each instance. Multi-label classification should not be confused with multiclass classification, which is the problem is categorizing instances into more than two classes.
Several strategies exist for multi-label classification; a common one is the one-vs.-all (or one-vs.-rest, OvA or OvR) strategy, which consists of training a single classifier for each class that can distinguish that class from all others.[1]
References
- ^ Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze (2008). Introduction to Information Retrieval. Cambridge University Press.
Categories:
- Artificial intelligence stubs
- Classification algorithms
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