Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Tutorial on Support Vector Machines

Version 1 : Received: 13 January 2022 / Approved: 17 January 2022 / Online: 17 January 2022 (14:19:52 CET)

How to cite: Bridgelall, R. Tutorial on Support Vector Machines. Preprints 2022, 2022010232 (doi: 10.20944/preprints202201.0232.v1). Bridgelall, R. Tutorial on Support Vector Machines. Preprints 2022, 2022010232 (doi: 10.20944/preprints202201.0232.v1).

Abstract

The aim of this tutorial is to help students grasp the theory and applicability of support vector machines (SVMs). The contribution is an intuitive style tutorial that helped students gain insights into SVM from a unique perspective. An internet search will reveal many videos and articles on SVM, but many of them give simplified explanations that leave gaps in the derivations that beginning students cannot fill. Most free tutorials lack guidance on practical applications and considerations. The software wrappers in popular programming libraries such as Python and R hide many of the operational complexities. Free software tools often use default parameters that ignore domain knowledge or leave knowledge gaps about the important effects of SVM hyperparameters, resulting in misuse and subpar outcomes. The author uses this tutorial as a course reference for students studying artificial intelligence and machine learning. The tutorial derives the classic SVM classifier from first principles and then derives the practical form that a computer uses to train a classification model. An intuitive explanation about confusion matrices, F1 score, and the AUC metric extend insights into the inherent tradeoff between sensitivity and specificity. A discussion about cross-validation provides a basic understanding of hyperparameter tuning to maximize generalization by balancing underfitting and overfitting. Even seasoned self-learners with advanced statistical backgrounds have gained insights from this tutorial style of intuitive explanations, with all related considerations for tuning and performance evaluations in one place.

Keywords

artificial intelligence; data science; Kernel trick; machine learning; pedagogy

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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