spam: Spam E-mail Database

In kernlab: Kernel-Based Machine Learning Lab

Spam E-mail Database

Description

A data set collected at Hewlett-Packard Labs, that classifies 4601 e-mails as spam or non-spam. In addition to this class label there are 57 variables indicating the frequency of certain words and characters in the e-mail.

Usage

data(spam)

Format

A data frame with 4601 observations and 58 variables.

The first 48 variables contain the frequency of the variable name (e.g., business) in the e-mail. If the variable name starts with num (e.g., num650) the it indicates the frequency of the corresponding number (e.g., 650). The variables 49-54 indicate the frequency of the characters ‘;’, ‘(’, ‘[’, ‘!’, ‘$’, and ‘#’. The variables 55-57 contain the average, longest and total run-length of capital letters. Variable 58 indicates the type of the mail and is either "nonspam" or "spam", i.e. unsolicited commercial e-mail.

Details

The data set contains 2788 e-mails classified as "nonspam" and 1813 classified as "spam".

The “spam” concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography... This collection of spam e-mails came from the collectors' postmaster and individuals who had filed spam. The collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.

Source

\Sexpr[results=rd]{tools:::Rd_expr_doi("10.24432/C53G6X")}

References

T. Hastie, R. Tibshirani, J.H. Friedman. The Elements of Statistical Learning. Springer, 2001.

kernlab documentation built on Sept. 12, 2024, 6:51 a.m.