Linear 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 6 in Machine Learning for Practical Decision Making, 2022, pp 195-230 from Springer

Abstract: Abstract Regression aims at predicting a future value, so the outcome we are trying to predict is a number, not a class. Many problems can be reduced to predicting a number; for example, predicting the median house value, or predicting the number of people who will be infected by a virus, or predicting the rate of readmission to a hospital in a certain season, etc. In all these examples, our outcome is a number, so regression can be used to predict the outcome. In other words, we can use regression to build a model that can predict (with a certain likelihood of success) the outcome based on the existing features (i.e., dataset attributes). The situation resembles estimating a function f that takes many variables as an input and computes a number that estimates (with a certain likelihood of success) what the future outcome will be. The statement “with a certain likelihood of success” refers to the probability that the model (i.e., function) is correct; that model’s probability of success can be computed when we build the model.

Date: 2022
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DOI: 10.1007/978-3-031-16990-8_6

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