Non-Linear Predictive Model
A Non-Linear Predictive Model is a Predictive Model that does not a assume a linear combination between variables.
- Example(s):
- Counter-Example(s):
- See: Predictive Analytics, Predictive Models, Predictive Software Model, Negative Predictive Value, Positive Predictive Value, Predictive Control Algorithm, Predictive Regression Function.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Predictive_analytics#Predictive_models Retrieved:2019-2-21.
- Predictive modelling uses predictive models to analyze the relationship between the specific performance of a unit in a sample and one or more known attributes or features of the unit. The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance. This category encompasses models in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, or fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios.
The available sample units with known attributes and known performances is referred to as the "training sample". The units in other samples, with known attributes but unknown performances, are referred to as "out of [training] sample" units. The out of sample units do not necessarily bear a chronological relation to the training sample units. For example, the training sample may consist of literary attributes of writings by Victorian authors, with known attribution, and the out-of sample unit may be newly found writing with unknown authorship; a predictive model may aid in attributing a work to a known author. Another example is given by analysis of blood splatter in simulated crime scenes in which the out of sample unit is the actual blood splatter pattern from a crime scene. The out of sample unit may be from the same time as the training units, from a previous time, or from a future time.
- Predictive modelling uses predictive models to analyze the relationship between the specific performance of a unit in a sample and one or more known attributes or features of the unit. The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance. This category encompasses models in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, or fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios.
1999
- (Zaiane, 1999) ⇒ Osmar Zaiane. (1999). “Glossary of Data Mining Terms." University of Alberta, Computing Science CMPUT-690: Principles of Knowledge Discovery in Databases.
- QUOTE: Non-Linear Predictive Models: An analytical model that does not assume linear relationships in the coefficients of the variables being studied(...)
Artificial Neural Networks:Non-linear predictive models that learn through training and resemble biological neural networks in structure.
- QUOTE: Non-Linear Predictive Models: An analytical model that does not assume linear relationships in the coefficients of the variables being studied(...)