Predictive Power
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A Predictive Power is an evaluation function for the performance of a prediction algorithm.
- AKA: Prediction Power Measure.
- Context:
- It can range from being an a priori to a posteriori measure.
- …
- Counter-Example(s):
- a p-Value.
- See: Performance, Prediction Algorithm, Statistical Confidence, Predictive Rule.
References
2011
- (Shmueli & Koppius, 2011) ⇒ Galit Shmueli, and Otto R. Koppius. (2011). “Predictive Analytics in Information Systems Research.” In: MIS Quarterly Journal, 35(3).
- QUOTE: … Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. … Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well.
2009
- (Monreale et al., 2009) ⇒ Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. (2009). “WhereNext: A Location Predictor on Trajectory Pattern Mining.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557091
- QUOTE: In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns (...) we define an evaluation function for estimating the predictive power of the collection of T-patterns before the creation of the T-pattern Tree and the quality measures of the performance of the prediction to be done a posteriori.