Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency

Abstract

Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.