Statistical Learning

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3 domain-specific abstractions whose origin domain is Statistical Learning.

• Bayes classifier — The decision rule that assigns each feature vector to the class with greatest posterior probability, minimizing expected classification loss when the true class distributions and loss function are known.
• Binary classification — Assign observations to exactly two declared classes through a learned or specified decision rule, keeping scores, thresholds, reference labels, asymmetric errors, prevalence, and evaluation population distinct.
• Oversampling and undersampling in data analysis — Resampling strategies that alter class frequencies in a dataset by adding or repeating minority observations or removing majority observations.