Predictive modelling
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]
In many cases, the model is chosen on the basis of
Models can use one or more
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".
Models
Nearly any
Applications
Uplift modelling
Uplift modelling is a technique for modelling the change in probability caused by an action. Typically this is a marketing action such as an offer to buy a product, to use a product more or to re-sign a contract. For example, in a retention campaign you wish to predict the change in probability that a customer will remain a customer if they are contacted. A model of the change in probability allows the retention campaign to be targeted at those customers on whom the change in probability will be beneficial. This allows the retention programme to avoid triggering unnecessary churn or customer attrition without wasting money contacting people who would act anyway.
Archaeology
Predictive modelling in
Generally, predictive modelling in archaeology is establishing statistically valid causal or covariable relationships between natural proxies such as soil types, elevation, slope, vegetation, proximity to water, geology, geomorphology, etc., and the presence of archaeological features. Through analysis of these quantifiable attributes from land that has undergone archaeological survey, sometimes the "archaeological sensitivity" of unsurveyed areas can be anticipated based on the natural proxies in those areas. Large land managers in the United States, such as the Bureau of Land Management (BLM), the Department of Defense (DOD),[6][7] and numerous highway and parks agencies, have successfully employed this strategy. By using predictive modelling in their cultural resource management plans, they are capable of making more informed decisions when planning for activities that have the potential to require ground disturbance and subsequently affect archaeological sites.
Customer relationship management
Predictive modelling is used extensively in analytical customer relationship management and data mining to produce customer-level models that describe the likelihood that a customer will take a particular action. The actions are usually sales, marketing and customer retention related.
For example, a large
Auto insurance
Predictive modelling is utilised in
Health care
In 2009
In 2018, Banerjee et al.
The first clinical prediction model reporting guidelines were published in 2015 (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD)), and have since been updated.[10]
Predictive modelling has been used to estimate surgery duration.
Algorithmic trading
Lead tracking systems
Predictive modelling gives
Notable failures of predictive modeling
Although not widely discussed by the mainstream predictive modeling community, predictive modeling is a methodology that has been widely used in the financial industry in the past and some of the major failures contributed to the
- Bond rating. macroeconomic data. The rating agencies failed with their ratings on the US$600 billion mortgage backed Collateralized Debt Obligation (CDO) market. Almost the entire AAA sector (and the super-AAA sector, a new rating the rating agencies provided to represent super safe investment) of the CDO market defaulted or severely downgraded during 2008, many of which obtained their ratings less than just a year previously.[citation needed]
- So far, no statistical models that attempt to predict equity market prices based on historical data are considered to consistently make correct predictions over the long term. One particularly memorable failure is that of price spreads between different securities. The models produced impressive profits until a major debacle that caused the then Federal Reserve chairman Alan Greenspan to step in to broker a rescue plan by the Wall Street broker dealers in order to prevent a meltdown of the bond market.[citation needed]
Possible fundamental limitations of predictive models based on data fitting
History cannot always accurately predict the future. Using relations derived from historical data to predict the future implicitly assumes there are certain lasting conditions or constants in a complex system. This almost always leads to some imprecision when the system involves people.[citation needed]
Algorithms can be defeated adversarially. After an algorithm becomes an accepted standard of measurement, it can be taken advantage of by people who understand the algorithm and have the incentive to fool or manipulate the outcome. This is what happened to the CDO rating described above. The CDO dealers actively fulfilled the rating agencies' input to reach an AAA or super-AAA on the CDO they were issuing, by cleverly manipulating variables that were "unknown" to the rating agencies' "sophisticated" models.[citation needed]
See also
- Calibration (statistics)
- Prediction interval
- Predictive analytics
- Predictive inference
- Statistical learning theory
- Statistical model
References
- ISBN 978-0-412-03471-8.
- ISBN 978-1137379276.
- ISBN 978-1439858011.
- ^ Cox, D. R. (2006). Principles of Statistical Inference. Cambridge University Press. p. 2.
- ^ Willey, Gordon R. (1953), "Prehistoric Settlement Patterns in the Virú Valley, Peru", Bulletin 155. Bureau of American Ethnology
- ^ Heidelberg, Kurt, et al. "An Evaluation of the Archaeological Sample Survey Program at the Nevada Test and Training Range", SRI Technical Report 02-16, 2002
- ^ Jeffrey H. Altschul, Lynne Sebastian, and Kurt Heidelberg, "Predictive Modeling in the Military: Similar Goals, Divergent Paths", Preservation Research Series 1, SRI Foundation, 2004
- ^ "Hospital Uses Data Analytics and Predictive Modeling To Identify and Allocate Scarce Resources to High-Risk Patients, Leading to Fewer Readmissions". Agency for Healthcare Research and Quality. 2014-01-29. Retrieved 2019-03-19.
- PMID 29968730.
- PMID 38626948.
- ^ "Predictive-Model Based Trading Systems, Part 1 - System Trader Success". System Trader Success. 2013-07-22. Retrieved 2016-11-25.
- ^ "Predictive Modeling for Call Tracking". Phonexa. 2019-08-22. Retrieved 2021-02-25.
Further reading
- Clarke, Bertrand S.; Clarke, Jennifer L. (2018), Predictive Statistics, Cambridge University Press
- Iglesias, Pilar; Sandoval, Mônica C.; Pereira, Carlos Alberto de Bragança (1993), "Predictive likelihood in finite populations", JSTOR 43600831
- Kelleher, John D.; Mac Namee, Brian; D'Arcy, Aoife (2015), Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, worked Examples and Case Studies, MIT Press
- Kuhn, Max; Johnson, Kjell (2013), Applied Predictive Modeling, Springer
- Shmueli, G. (2010), "To explain or to predict?", S2CID 15900983