从效率角度评估核技巧 SVM、市场篮子分析和 Naive Bayes 的预测建模性能

Safiye Turgay, Metehan Han, Suat Erdoğan, Esma Sedef Kara, Recep Yilmaz
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引用次数: 0

摘要

在预测建模的诸多相应问题中,几种方法的效率和效果最为重要。本研究对三种不同的方法进行了全面的比较分析:最后,引用了核伎俩支持向量机(SVM)、市场篮子分析(MBA)和天真贝叶斯分类器。我们的研究旨在明确这些方法在提供正确信息、准确性、计算复杂性以及在不同领域的适用程度等方面的优势和好处。核函数 SVM 因其处理非线性数据传输到高维空间问题的能力而得到认可,其本质是在复杂分类中对它们的期望。它们基于机器学习的特点依赖于详细制定精确的混淆决策边界,并分析了不同的核函数,这些核函数的功能更加强大。市场篮子分析是一种复杂的工具,它揭示了交易中提供的数据之间的关系,其性能帮助我发现了一种预测客户行为的方法。这项技术能让痛点推荐系统和领导者利用其发现的购买习惯做出战略性商业决策。这项研究的有效性归功于处理大量数据、寻找有意义的模式以及发布有益的建议。与此同时,作者还将尝试了解一种贝叶斯分类器,它属于概率模型的一种,因其简单、高效而被广泛使用。作者概述了在不同分类器中使用贝叶斯分类器时,其属性独立性概念假设的优缺点。研究仔细检查了它们在文本分类和图像识别中的有效性,以及适应不同任务的能力。通过这种方式,调查旨在找出如何使应用程序更适合各种用途。这项研究对读者的能力有很大的帮助,读者可以很好地了解模型的准确性、效率以及模型适合的数据类型、领域或问题,从而决定是否选择特定的模型。
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Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency
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.
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