informaatioarvoksi

Informaatioarvoksi is a concept used in information theory, data science and statistics to quantify the value of information provided by a signal, feature, or message for reducing uncertainty in a decision problem. It captures how much expected reduction in uncertainty a given piece of information yields relative to a prior state, and it is often used to compare information sources or predictors in modelling tasks.

Historically, informaatioarvoksi draws on Shannon's information theory and Bayesian decision theory. In practice, it is frequently operationalized as information value (IV) in predictive modelling and feature selection. The term is common in fields such as credit scoring, marketing analytics and risk assessment, where the aim is to identify features that improve predictive accuracy.

Measurement: a widely used definition in data mining is IV = sum_i ( (p1_i - p0_i) × ln(p1_i/p0_i) ), where p1_i is the proportion of positive outcomes (or events of interest) in bin i and p0_i the proportion of negative outcomes. Alternatives interpret informaatioarvoksi as the difference in class distributions or as the expected reduction in entropy: IV = H(Y) - E[ H(Y|X) ]. The base of the logarithm determines the unit (bits for base 2, nats for natural log).

Applications and limitations: IV is used to rank and select features for predictive models, to gauge the strength of signals in marketing experiments, credit risk assessment and information retrieval. It is a heuristic measure and can be sensitive to how data are binned, to sample size and to class imbalance. It should be interpreted alongside other performance metrics rather than as a definitive performance measure.

See also: entropy, mutual information, Kullback-Leibler divergence, weight of evidence, information theory.