Logistic Regression

Christo El Morr, Manar Jammal, Hossam Ali-Hassan and Walid El-Hallak ()
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Christo El Morr: York University
Manar Jammal: York University
Hossam Ali-Hassan: York University, Glendon Campus
Walid El-Hallak: Ontario Health

Chapter Chapter 7 in Machine Learning for Practical Decision Making, 2022, pp 231-249 from Springer

Abstract: Abstract Linear regression modeling is well suited to predicting continuous data where the outcome y is a real number (i.e., y ∈ ℝ). Logistic regression is a modeling technique for binary outcomes (i.e., yes/no, true/false, 1/0). Such outcomes are needed in many domains: public health officials might want to know the likelihood that a person will contract COVID-19 if she is a doctor in Ontario; a hospital would like to know if a discharged patient is more likely to be readmitted or not; a company would like to know if a customer visiting its website is more likely to order; a bank would like to know if a customer is more likely to default on a loan or not. Logistic regression has been much used in the medical field and yielded impressive results [1–10].

Date: 2022
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DOI: 10.1007/978-3-031-16990-8_7

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