Naïve Bayes Classifier Model for Detecting Spam Mails

被引：0

作者：

Kumar S. ^{[1
]}

Gupta K. ^{[2
]}

Gupta M. ^{[1
]}

机构：

[1] Department of Statistics, Kirori Mal College, University of Delhi, Delhi

[2] Department of Mathematics, Kirori Mal College, University of Delhi, Delhi

来源：

Annals of Data Science | 2024年 / 11卷 / 06期

关键词：

Artificial intelligence; Machine learning; Naïve Bayes Classifier; Predictive analytics; Supervised machine learning;

D O I：

10.1007/s40745-023-00479-z

中图分类号：

学科分类号：

摘要：

In this paper, the machine learning algorithm Naive Bayes Classifier is applied to the Kaggle spam mails dataset to classify the emails in our inbox as spam or ham. The dataset is made up of two main attributes: type and text. The target variable "Type" has two factors: ham and spam. The text variable contains the text messages that will be classified as spam or ham. The results are obtained by employing two different Laplace values. It is up to the decision maker to select error tolerance in ham and spam messages derived from two different Laplace values. Computing software R is used for data analysis. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：1887 / 1897

页数：10

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