Abstract
In machine learning, regression analysis refers to a process for estimating the relationships between dependent variables and independent variables. This method is mainly used to predict and find the cause-and-effect relationship between variables. For example, in a linear regression, a researcher tries to find the line that best fits the data according to a certain mathematical criterion (see Fig. 3.1a).
Example of various regression problems. The x-axes are for the independent variables, and y-axes are for the dependent variables. (a) linear regression, (b) logistic regression, and (c) nonlinear regression using a polynomial kernel
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Ye, J.C. (2022). Linear, Logistic, and Kernel Regression. In: Geometry of Deep Learning. Mathematics in Industry, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-16-6046-7_3
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DOI: https://doi.org/10.1007/978-981-16-6046-7_3
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