Logistic regression

| issue = 7

| issue = 7

| pages = 511–24

| pages = 511–24

| 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 decisions taken 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 |bibcode=2013NHRev..14...11M |issn=1527-6988|url-access=subscription }}</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|url-access=subscription }}</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|url-access=subscription }}</ref> These models help in the development of reliable [[Emergency management|disaster managing plans]] and safer design for the [[built environment]].

| 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 for 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 decisions taken 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 |bibcode=2013NHRev..14...11M |issn=1527-6988|url-access=subscription }}</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|url-access=subscription }}</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|url-access=subscription }}</ref> These models help in the development of reliable [[Emergency management|disaster managing plans]] and safer design for the [[built environment]].

=== Supervised machine learning ===

=== Supervised machine learning ===