Spam Classification Task

Spam data set from the UCI machine learning repository (http://archive.ics.uci.edu/dataset/94/spambase). Data set collected at Hewlett-Packard Labs to classify emails as spam or non-spam. 57 variables indicate the frequency of certain words and characters in the e-mail. The positive class is set to "spam".

Format

R6::R6Class inheriting from TaskClassif.

Source

Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt. Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304

Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835

Preprocessing: Columns have been renamed. Preprocessed data taken from the kernlab package.

Dictionary

This Task can be instantiated via the dictionary mlr_tasks or with the associated sugar function tsk():

mlr_tasks$get("spam")
tsk("spam")

Meta Information

• Task type: “classif”

• Dimensions: 4601x58

• Properties: “twoclass”

• Has Missings: FALSE

• Target: “type”

• Features: “address”, “addresses”, “all”, “business”, “capitalAve”, “capitalLong”, “capitalTotal”, “charDollar”, “charExclamation”, “charHash”, “charRoundbracket”, “charSemicolon”, “charSquarebracket”, “conference”, “credit”, “cs”, “data”, “direct”, “edu”, “email”, “font”, “free”, “george”, “hp”, “hpl”, “internet”, “lab”, “labs”, “mail”, “make”, “meeting”, “money”, “num000”, “num1999”, “num3d”, “num415”, “num650”, “num85”, “num857”, “order”, “original”, “our”, “over”, “parts”, “people”, “pm”, “project”, “re”, “receive”, “remove”, “report”, “table”, “technology”, “telnet”, “will”, “you”, “your”

References

Dua, Dheeru, Graff, Casey (2017). “UCI Machine Learning Repository.” http://archive.ics.uci.edu/datasets.

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html

• Package mlr3data for more toy tasks.

• Package mlr3oml for downloading tasks from https://www.openml.org.

• Package mlr3viz for some generic visualizations.

• Dictionary of Tasks: mlr_tasks

• as.data.table(mlr_tasks) for a table of available Tasks in the running session (depending on the loaded packages).

• mlr3fselect and mlr3filters for feature selection and feature filtering.

• Extension packages for additional task types:

  • Unsupervised clustering: mlr3cluster

  • Probabilistic supervised regression and survival analysis: https://mlr3proba.mlr-org.com/.

Other Task: Task, TaskClassif, TaskRegr, TaskSupervised, TaskUnsupervised, california_housing, mlr_tasks, mlr_tasks_breast_cancer, mlr_tasks_german_credit, mlr_tasks_iris, mlr_tasks_mtcars, mlr_tasks_penguins, mlr_tasks_pima, mlr_tasks_sonar, mlr_tasks_wine, mlr_tasks_zoo