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| last2 = Cornfield| first2 = J| last3 = Kannel| first3 = W | doi= 10.1016/0021-9681(67)90082-3}}</ref> Another example might be to predict whether a Nepalese voter will vote Nepali Congress or Communist Party of Nepal or Any Other Party, based on age, income, sex, race, state of residence, votes in previous elections, etc.<ref name="rms" /> The technique can also be used in [[engineering]], especially for predicting the probability of failure of a given process, system or product.<ref name="strano05">{{cite journal | author = M. Strano | author2 = B.M. Colosimo | year = 2006 | title = Logistic regression analysis for experimental determination of forming limit diagrams | journal = International Journal of Machine Tools and Manufacture | volume = 46 | issue = 6 | pages = 673–682 | doi = 10.1016/j.ijmachtools.2005.07.005 }}</ref><ref name="safety">{{cite journal | last1 = Palei | first1 = S. K. | last2 = Das | first2 = S. K. | doi = 10.1016/j.ssci.2008.01.002 | title = Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: An approach | journal = Safety Science | volume = 47 | pages = 88–96 | year = 2009 }}</ref> It is also used in [[marketing]] applications such as prediction of a customer's propensity to purchase a product or halt a subscription, etc.<ref>{{cite book|title=Data Mining Techniques For Marketing, Sales and Customer Support|last= Berry |first=Michael J.A|publisher=Wiley|year=1997|page=10}}</ref> In [[economics]], it can be used to predict the likelihood of a person ending up in the labor force, and a business application would be to predict the likelihood of a homeowner defaulting on a [[mortgage]]. [[Conditional random field]]s, an extension of logistic regression to sequential data, are used in [[natural language processing]]. Disaster planners and engineers rely on these models to predict decision take by householders or building occupants in small-scale and large-scales evacuations ,such as building fires, wildfires, hurricanes among others.<ref>{{Cite journal |last1=Mesa-Arango |first1=Rodrigo |last2=Hasan |first2=Samiul |last3=Ukkusuri |first3=Satish V. |last4=Murray-Tuite |first4=Pamela |date=February 2013 |title=Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data |url=https://ascelibrary.org/doi/10.1061/%28ASCE%29NH.1527-6996.0000083 |journal=Natural Hazards Review |language=en |volume=14 |issue=1 |pages=11–20 |doi=10.1061/(ASCE)NH.1527-6996.0000083 |issn=1527-6988}}</ref><ref>{{Cite journal |last1=Wibbenmeyer |first1=Matthew J. |last2=Hand |first2=Michael S. |last3=Calkin |first3=David E. |last4=Venn |first4=Tyron J. |last5=Thompson |first5=Matthew P. |date=June 2013 |title=Risk Preferences in Strategic Wildfire Decision Making: A Choice Experiment with U.S. Wildfire Managers |url=https://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2012.01894.x |journal=Risk Analysis |language=en |volume=33 |issue=6 |pages=1021–1037 |doi=10.1111/j.1539-6924.2012.01894.x |pmid=23078036 |bibcode=2013RiskA..33.1021W |s2cid=45282555 |issn=0272-4332}}</ref><ref>{{Cite journal |last1=Lovreglio |first1=Ruggiero |last2=Borri |first2=Dino |last3=dell’Olio |first3=Luigi |last4=Ibeas |first4=Angel |date=2014-02-01 |title=A discrete choice model based on random utilities for exit choice in emergency evacuations |url=https://www.sciencedirect.com/science/article/pii/S0925753513002294 |journal=Safety Science |volume=62 |pages=418–426 |doi=10.1016/j.ssci.2013.10.004 |issn=0925-7535}}</ref> These models help in the development of reliable [[Emergency management|disaster managing plans]] and safer design for the [[built environment]].
=== Supervised
Logistic regression is a [[supervised machine learning]] algorithm widely used for [[binary classification]] tasks, such as identifying whether an email is spam or not and diagnosing diseases by assessing the presence or absence of specific conditions based on patient test results. This approach utilizes the logistic (or sigmoid) function to transform a linear combination of input features into a probability value ranging between 0 and 1. This probability indicates the likelihood that a given input corresponds to one of two predefined categories. The essential mechanism of logistic regression is grounded in the logistic function's ability to model the probability of binary outcomes accurately. With its distinctive S-shaped curve, the logistic function effectively maps any real-valued number to a value within the 0 to 1 interval. This feature renders it particularly suitable for binary classification tasks, such as sorting emails into "spam" or "not spam". By calculating the probability that the dependent variable will be categorized into a specific group, logistic regression provides a probabilistic framework that supports informed decision-making.<ref>{{Cite web |title=Logistic Regression |url=https://www.mastersindatascience.org/learning/machine-learning-algorithms/logistic-regression/ |access-date=2024-03-16 |website=CORP-MIDS1 (MDS) |language=en-US}}</ref>
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