Abstract
The previous chapter introduced all conceptual and numerical foundations for solving linear regression problems in the context of machine learning. There, the emphasis was on simple formulation that is easy to understand. However, machine learning regression offers many more methods and tools than already introduced. To this end, we will introduce model formulations that can easily be generalized and that additionally can also be efficiently implemented as vectorized Python code. Furthermore, the concept of basis functions offers a multitude of more advanced regression models such as piecewise formulations or non-parametric kernel regression.
The purpose of computing is insight, not numbers.
Richard Hamming (1915–1998)
American mathematician
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Notes
- 1.
Recall that, \(\mathcal {J}(w_0, \ldots , w_n;\; \mathscr {D}^{\mathrm {train}})\) would be pronounced as “The resulting cost \({\mathcal {J}}\) for given training data is a function of the weights ….”
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Sandfeld, S. (2024). Advanced Methods and Topics of Regression. In: Materials Data Science. The Materials Research Society Series. Springer, Cham. https://doi.org/10.1007/978-3-031-46565-9_13
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DOI: https://doi.org/10.1007/978-3-031-46565-9_13
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