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4. Introduction to Shallow Supervised Methods

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Karol Przystalski
Maciej J. Ogorzałek
Jan K. Argasiński
Wiesław Chmielnicki
Published in
Pattern Recognition Primer
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the foundational methods of supervised machine learning, focusing on linear classifiers, regression techniques, and nearest neighbor classifiers. It begins with an explanation of linear classifiers, such as Fisher's Linear Discriminant, which reduces dimensionality to simplify classification tasks. The chapter then explores regression methods, including linear and logistic regression, highlighting their use in predicting future values and their implementation through Python code. Additionally, it covers nearest neighbor classifiers, demonstrating how they assign labels based on proximity to known data points. Practical examples, such as the Iris dataset and Titanic survival prediction, are used to illustrate these methods. The chapter also discusses advanced topics like Lasso and Ridge regression, which address overfitting and model complexity. By the end, readers will have a solid understanding of these fundamental techniques and their applications in real-world scenarios.
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previous chapter Unsupervised Learning
next chapter Decision Trees
Title
Introduction to Shallow Supervised Methods
Authors
Karol Przystalski
Maciej J. Ogorzałek
Jan K. Argasiński
Wiesław Chmielnicki
Copyright Year
2026
Book
Pattern Recognition Primer

Print ISBN: 978-3-031-91815-5

Electronic ISBN: 978-3-031-91816-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-031-91816-2_4
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