# Predictive Model Creation Task

A Predictive Model Creation Task is a model creation task that can produce a predictive model.

**Context:**- Optional Input: a Predictive Metamodel (such as a SIR model).
- Output: a creates a predictive model structure.
- It can be solved by a Predictive Model Creation System (that implements a predictive model creation algorithm).
- It can be associated to a Predictive Model Evaluation Task.
- It can range from being an Ad-hoc Predictive Model Creation Task to being a Formal Predictive Model Creation Task (such as a repeatable predictive model creation task).
- It can range from being a Black-Box Model Creation Task to being a Interpretable Model Creation Task (such as explanatory predictive modeling for explanatory predictive models).
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**Example(s):****Counter-Example(s):****See:**Learning Task, Inductive Reasoning, Estimation Task, Predictive Analytics, Correlation Does Not Imply Causation, Detection Theory, Predictive Analytics.

## References

### 2020

- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/predictive_modelling Retrieved:2020-4-6.
**Predictive modeling**uses statistics to predict outcomes. 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.In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.

Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set. For example, a model might be used to determine whether an email is spam or "ham" (non-spam).

Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learning, as it is more commonly referred to in academic or research and development contexts. When deployed commercially, predictive modelling is often referred to as predictive analytics.

Predictive modelling is often contrasted with causal modelling/analysis. In the former, one may be entirely satisfied to make use of indicators of, or proxies for, the outcome of interest. In the latter, one seeks to determine true cause-and-effect relationships. This distinction has given rise to a burgeoning literature in the fields of research methods and statistics and to the common statement that "correlation does not imply causation”.