The Predictive Model:
What is a Predictive Model?
A predictive model uses statistics to predict outcomes. First you set your information that you want to be mapped, be it a customers age, gender, or even favorite ice cream flavor, as long as it is pertinent to your end goal. These factors are known as predictors. A predictive model then uses these predictors you input about a particular issue or series of actions to show you what the probability of a particular outcome will be.
The predictive model is a valuable tool that is in widespread use in modern business, industry, and a wide range of social and governmental organizations. It is a proven method for anticipating the future outcomes of a wide variety of actions and can be applied to any type of event.
Depending on the definitional boundaries that you set, predictive modeling is often synonymous with, or overlaps into what is widely known as machine learning. When it is used in commercial enterprise, predictive modelling is commonly called predictive analytics. For prediction purposes, almost any regression model may be employed.
There are two basic types of predictive models. They are parametric and non-parametric. A third type, semi-parametric, combines elements of the two others.
Parametric models deal with assumptions made regarding a number of population parameters by which the underlying distributions are characterized. Non-parametric regressions, on the other hand, do not make as many assumptions as their parametric counterparts. Many types of regression model are effective. They are key components of predictive modeling.
Used properly predictive modeling can be an invaluable tool for any business that wishes to anticipate the needs of their customers and the market.