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Classification vs. Prediction – Statistical Thinking

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It is important to distinguish prediction and classification. In many decisionmaking contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. The classification rule must be reformulated if costs/utilities or sampling criteria change. Predictions are separate from decisions and can be used by any decision maker. Classification is best used with non-stochastic/deterministic outcomes that occur in say 0.3 - 0.7 of the observations, and not when the simplest classifer (always outputting “positive” or always outputting “negative”) is highly accurate or when two individuals with identical inputs can easily have different outcomes. For these situations, modeling tendencies (i.e., probabilities) is key. Classification should be used when outcomes are distinct and predictors are strong enough to provide, for all subjects, a probability near 1.0 for one of the

Classification vs. Prediction – Statistical Thinking It is important to distinguish prediction and classification. In many decisionmaking contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. The classification rule must be reformulated if costs/utilities or sampling criteria change. Predictions are separate from decisions and can be used by any decision maker. Classification is best used with non-stochastic/deterministic outcomes that occur in say 0.3 - 0.7

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