{ "data_id": "44", "name": "spambase", "exact_name": "spambase", "version": 1, "version_label": "1", "description": "**Author**: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt \r\n**Source**: [UCI](https:\/\/archive.ics.uci.edu\/ml\/datasets\/spambase) \r\n**Please cite**: [UCI](https:\/\/archive.ics.uci.edu\/ml\/citation_policy.html)\r\n\r\nSPAM E-mail Database \r\nThe \"spam\" concept is diverse: advertisements for products\/websites, make money fast schemes, chain letters, pornography... Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our 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.\r\n \r\nFor background on spam: \r\nCranor, Lorrie F., LaMacchia, Brian A. Spam! Communications of the ACM, 41(8):74-83, 1998. \r\n\r\n### Attribute Information: \r\nThe last column denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. Most of the attributes indicate whether a particular word or character was frequently occurring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters. \r\n\r\nFor the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes: \r\n\r\n48 continuous real [0,100] attributes of type \r\nword_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) \/ total number of words in e-mail. A \"word\" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.\r\n \r\n6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) \/ total characters in e-mail\r\n \r\n1 continuous real [1,...] attribute of type capital_run_length_average\r\n = average length of uninterrupted sequences of capital letters\r\n \r\n1 continuous integer [1,...] attribute of type capital_run_length_longest\r\n = length of longest uninterrupted sequence of capital letters\r\n \r\n1 continuous integer [1,...] attribute of type capital_run_length_total\r\n = sum of length of uninterrupted sequences of capital letters\r\n = total number of capital letters in the e-mail\r\n \r\n1 nominal {0,1} class attribute of type spam\r\n = denotes whether the e-mail was considered spam (1) or not (0), \r\n i.e. unsolicited commercial e-mail. ", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": null, "contributor": null, "date": "2014-04-06 23:22:41", "update_comment": null, "last_update": "2014-04-06 23:22:41", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/44\/dataset_44_spambase.arff", "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 156158, "suggest": { "input": [ "spambase", "SPAM E-mail Database The \"spam\" concept is diverse: advertisements for products\/websites, make money fast schemes, chain letters, pornography... Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our 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-sp " ], "weight": 5 }, "qualities": { "NumberOfInstances": 4601, "NumberOfFeatures": 58, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 57, "NumberOfSymbolicFeatures": 1, "MinSkewnessOfNumericAtts": 1.5916742687064245, "PercentageOfInstancesWithMissingValues": 0, "Quartile3AttributeEntropy": null, "RandomTreeDepth1ErrRate": 0.10410780265159748, "EquivalentNumberOfAtts": null, "MaxNominalAttDistinctValues": 2, "MinStdDevOfNumericAtts": 0.07627427063724908, "PercentageOfMissingValues": 0, "Quartile3KurtosisOfNumericAtts": 299.0723734257733, "AutoCorrelation": 0.9997826086956522, "RandomTreeDepth1Kappa": 0.781973609246172, "J48.00001.AUC": 0.9245266333296668, "MaxSkewnessOfNumericAtts": 31.062064279039635, "MinorityClassPercentage": 39.404477287546186, "PercentageOfNumericFeatures": 98.27586206896551, "Quartile3MeansOfNumericAtts": 0.24413062377743908, 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0.31695822185668954, "REPTreeDepth2AUC": 0.9386679853220129, "CfsSubsetEval_kNN1NErrRate": 0.08563355792219082, "kNN1NAUC": 0.8937334657000572, "J48.001.Kappa": 0.82347465212921, "MeanSkewnessOfNumericAtts": 11.186639096029253, "Quartile2AttributeEntropy": null, "REPTreeDepth2ErrRate": 0.10345577048467725, "CfsSubsetEval_kNN1NKappa": 0.8208876445659258, "kNN1NErrRate": 0.10736796348619865, "MajorityClassPercentage": 60.59552271245382, "MeanStdDevOfNumericAtts": 15.193997694546747, "MinAttributeEntropy": null, "Quartile2KurtosisOfNumericAtts": 127.37652934849572, "REPTreeDepth2Kappa": 0.7807805902062573, "ClassEntropy": 0.9673602371807668, "kNN1NKappa": 0.775167746729542, "MajorityClassSize": 2788, "MinKurtosisOfNumericAtts": 5.257394367988116, "Quartile2MeansOfNumericAtts": 0.10285155400999912, "REPTreeDepth3AUC": 0.9386679853220129, "DecisionStumpAUC": 0.7941574124705914, "MaxAttributeEntropy": null, "MinMeansOfNumericAtts": 0.005444468593783957, "Quartile2MutualInformation": null, 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"study_37", "uploader": "1" }, { "tag": "study_41", "uploader": "1" }, { "tag": "study_52", "uploader": "64" }, { "tag": "study_7", "uploader": "64" }, { "tag": "study_70", "uploader": "1856" }, { "tag": "study_98", "uploader": "1935" }, { "tag": "study_99", "uploader": "1" }, { "tag": "uci", "uploader": "1" } ], "features": [ { "name": "class", "index": "57", "type": "nominal", "distinct": "2", "missing": "0", "target": "1", "distr": [ [ "0", "1" ], [ [ "2788", "0" ], [ "0", "1813" ] ] ] }, { "name": "word_freq_make", "index": "0", "type": "numeric", "distinct": "142", "missing": "0", "min": "0", "max": "5", "mean": "0", "stdev": "0" }, { "name": "word_freq_address", "index": "1", "type": "numeric", "distinct": "171", "missing": "0", "min": "0", "max": "14", "mean": "0", "stdev": "1" }, { "name": "word_freq_all", "index": "2", "type": "numeric", "distinct": "214", "missing": "0", "min": "0", "max": "5", "mean": "0", "stdev": "1" }, { "name": "word_freq_3d", "index": "3", "type": 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"type": "numeric", "distinct": "188", "missing": "0", "min": "0", "max": "7", "mean": "0", "stdev": "0" }, { "name": "word_freq_parts", "index": "37", "type": "numeric", "distinct": "53", "missing": "0", "min": "0", "max": "8", "mean": "0", "stdev": "0" }, { "name": "word_freq_pm", "index": "38", "type": "numeric", "distinct": "163", "missing": "0", "min": "0", "max": "11", "mean": "0", "stdev": "0" }, { "name": "word_freq_direct", "index": "39", "type": "numeric", "distinct": "125", "missing": "0", "min": "0", "max": "5", "mean": "0", "stdev": "0" }, { "name": "word_freq_cs", "index": "40", "type": "numeric", "distinct": "108", "missing": "0", "min": "0", "max": "7", "mean": "0", "stdev": "0" }, { "name": "word_freq_meeting", "index": "41", "type": "numeric", "distinct": "186", "missing": "0", "min": "0", "max": "14", "mean": "0", "stdev": "1" }, { "name": "word_freq_original", "index": "42", "type": "numeric", "distinct": "136", "missing": "0", "min": "0", "max": "4", "mean": "0", 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"195" }, { "name": "capital_run_length_total", "index": "56", "type": "numeric", "distinct": "919", "missing": "0", "min": "1", "max": "15841", "mean": "283", "stdev": "606" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 3, "nr_of_downloads": 77, "total_downloads": 89, "reach": 80, "reuse": 2, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 2 }