Data
spambase

spambase

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_14 study_15 study_20 study_34 study_37 study_41 study_52 study_7 study_70 study_98 study_99 uci
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Author: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt Source: [UCI](https://archive.ics.uci.edu/ml/datasets/spambase) Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) 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-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter. For background on spam: Cranor, Lorrie F., LaMacchia, Brian A. Spam! Communications of the ACM, 41(8):74-83, 1998. ### Attribute Information: The 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. For the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes: 48 continuous real [0,100] attributes of type word_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. 6 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 1 continuous real [1,...] attribute of type capital_run_length_average = average length of uninterrupted sequences of capital letters 1 continuous integer [1,...] attribute of type capital_run_length_longest = length of longest uninterrupted sequence of capital letters 1 continuous integer [1,...] attribute of type capital_run_length_total = sum of length of uninterrupted sequences of capital letters = total number of capital letters in the e-mail 1 nominal {0,1} class attribute of type spam = denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.

58 features

class (target)nominal2 unique values
0 missing
word_freq_makenumeric142 unique values
0 missing
word_freq_addressnumeric171 unique values
0 missing
word_freq_allnumeric214 unique values
0 missing
word_freq_3dnumeric43 unique values
0 missing
word_freq_ournumeric255 unique values
0 missing
word_freq_overnumeric141 unique values
0 missing
word_freq_removenumeric173 unique values
0 missing
word_freq_internetnumeric170 unique values
0 missing
word_freq_ordernumeric144 unique values
0 missing
word_freq_mailnumeric245 unique values
0 missing
word_freq_receivenumeric113 unique values
0 missing
word_freq_willnumeric316 unique values
0 missing
word_freq_peoplenumeric158 unique values
0 missing
word_freq_reportnumeric133 unique values
0 missing
word_freq_addressesnumeric118 unique values
0 missing
word_freq_freenumeric253 unique values
0 missing
word_freq_businessnumeric197 unique values
0 missing
word_freq_emailnumeric229 unique values
0 missing
word_freq_younumeric575 unique values
0 missing
word_freq_creditnumeric148 unique values
0 missing
word_freq_yournumeric401 unique values
0 missing
word_freq_fontnumeric99 unique values
0 missing
word_freq_000numeric164 unique values
0 missing
word_freq_moneynumeric143 unique values
0 missing
word_freq_hpnumeric395 unique values
0 missing
word_freq_hplnumeric281 unique values
0 missing
word_freq_georgenumeric240 unique values
0 missing
word_freq_650numeric200 unique values
0 missing
word_freq_labnumeric156 unique values
0 missing
word_freq_labsnumeric179 unique values
0 missing
word_freq_telnetnumeric128 unique values
0 missing
word_freq_857numeric106 unique values
0 missing
word_freq_datanumeric184 unique values
0 missing
word_freq_415numeric110 unique values
0 missing
word_freq_85numeric177 unique values
0 missing
word_freq_technologynumeric159 unique values
0 missing
word_freq_1999numeric188 unique values
0 missing
word_freq_partsnumeric53 unique values
0 missing
word_freq_pmnumeric163 unique values
0 missing
word_freq_directnumeric125 unique values
0 missing
word_freq_csnumeric108 unique values
0 missing
word_freq_meetingnumeric186 unique values
0 missing
word_freq_originalnumeric136 unique values
0 missing
word_freq_projectnumeric160 unique values
0 missing
word_freq_renumeric230 unique values
0 missing
word_freq_edunumeric227 unique values
0 missing
word_freq_tablenumeric38 unique values
0 missing
word_freq_conferencenumeric106 unique values
0 missing
char_freq_%3Bnumeric313 unique values
0 missing
char_freq_%28numeric641 unique values
0 missing
char_freq_%5Bnumeric225 unique values
0 missing
char_freq_%21numeric964 unique values
0 missing
char_freq_%24numeric504 unique values
0 missing
char_freq_%23numeric316 unique values
0 missing
capital_run_length_averagenumeric2161 unique values
0 missing
capital_run_length_longestnumeric271 unique values
0 missing
capital_run_length_totalnumeric919 unique values
0 missing

107 properties

4601
Number of instances (rows) of the dataset.
58
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
57
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
5.85
First quartile of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
11.19
Mean skewness among attributes of the numeric type.
0.32
First quartile of standard deviation of attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.09
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
15.19
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.11
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
60.6
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
127.38
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Entropy of the target attribute values.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2788
Number of instances belonging to the most frequent class.
5.26
Minimum kurtosis among attributes of the numeric type.
0.1
Second quartile (Median) of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.01
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1480.64
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
9.72
Second quartile (Median) of skewness among attributes of the numeric type.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
283.29
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
1.72
Percentage of binary attributes.
0.44
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
1.59
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
0.08
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
299.07
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
31.06
Maximum skewness among attributes of the numeric type.
39.4
Percentage of instances belonging to the least frequent class.
98.28
Percentage of numeric attributes.
0.24
Third quartile of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
606.35
Maximum standard deviation of attributes of the numeric type.
1813
Number of instances belonging to the least frequent class.
1.72
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
13.65
Third quartile of skewness among attributes of the numeric type.
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
241.17
Mean kurtosis among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
50.66
First quartile of kurtosis among attributes of the numeric type.
0.84
Third quartile of standard deviation of attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6.15
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.06
First quartile of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

24 tasks

92713 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
58350 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
367 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
367 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
215 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
374 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
207 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1319 runs - target_feature: class
1309 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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