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Spam data set from the UCI machine learning repository ( 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".


R6::R6Class inheriting from TaskClassif.


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 650-857-7835

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


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


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”


Dua, Dheeru, Graff, Casey (2017). “UCI Machine Learning Repository.”

See also

Other Task: Task, TaskClassif, TaskRegr, TaskSupervised, TaskUnsupervised, mlr_tasks, mlr_tasks_boston_housing, 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