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Predictive modelling uses statistics to predict outcomes. [1] Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. [2]
For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. [4] The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that ...
Predicted outcome value theory is an alternative to uncertainty reduction theory, which Charles R. Berger and Richard J. Calabrese introduced in 1975. Uncertainty reduction theory states that the driving force in initial interactions is to collect information to predict attitudes and behaviors for future relationship development.
Regression models predict a value of the Y variable given known values of the X variables. Prediction within the range of values in the dataset used for model-fitting is known informally as interpolation. Prediction outside this range of the data is known as extrapolation. Performing extrapolation relies strongly on the regression assumptions.
Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. [6]
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term.
Probabilistic forecasting summarizes what is known about, or opinions about, future events. In contrast to single-valued forecasts (such as forecasting that the maximum temperature at a given site on a given day will be 23 degrees Celsius, or that the result in a given football match will be a no-score draw), probabilistic forecasts assign a probability to each of a number of different ...
Other types of forecasting include forecasting models designed to predict the outcomes of international relations or bargaining events. One notable example is the expected utility model developed by American political scientist Bruce Bueno de Mesquita, which solves for the Bayesian Perfect Equilibria outcome of unidimensional policy events ...
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