diff options
| author | Patrick Simianer <p@simianer.de> | 2014-06-14 16:46:27 +0200 |
|---|---|---|
| committer | Patrick Simianer <p@simianer.de> | 2014-06-14 16:46:27 +0200 |
| commit | 26c490f404731d053a6205719b6246502c07b449 (patch) | |
| tree | 3aa721098f1251dfbf2249ecd2736434c13b1d48 /data/spambase/spambase.DOCUMENTATION | |
init
Diffstat (limited to 'data/spambase/spambase.DOCUMENTATION')
| -rw-r--r-- | data/spambase/spambase.DOCUMENTATION | 142 |
1 files changed, 142 insertions, 0 deletions
diff --git a/data/spambase/spambase.DOCUMENTATION b/data/spambase/spambase.DOCUMENTATION new file mode 100644 index 0000000..cdd8b26 --- /dev/null +++ b/data/spambase/spambase.DOCUMENTATION @@ -0,0 +1,142 @@ +1. Title: SPAM E-mail Database
+
+2. Sources:
+ (a) Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt
+ Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304
+ (b) Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835
+ (c) Generated: June-July 1999
+
+3. Past Usage:
+ (a) Hewlett-Packard Internal-only Technical Report. External forthcoming.
+ (b) Determine whether a given email is spam or not.
+ (c) ~7% misclassification error.
+ False positives (marking good mail as spam) are very undesirable.
+ If we insist on zero false positives in the training/testing set,
+ 20-25% of the spam passed through the filter.
+
+4. Relevant Information:
+ The "spam" concept is diverse: advertisements for products/web
+ sites, 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.
+
+5. Number of Instances: 4601 (1813 Spam = 39.4%)
+
+6. Number of Attributes: 58 (57 continuous, 1 nominal class label)
+
+7. Attribute Information:
+The last column of 'spambase.data' 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 occuring 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.
+
+
+8. Missing Attribute Values: None
+
+9. Class Distribution:
+ Spam 1813 (39.4%)
+ Non-Spam 2788 (60.6%)
+
+
+Attribute Statistics:
+ Min: Max: Average: Std.Dev: Coeff.Var_%:
+1 0 4.54 0.10455 0.30536 292
+2 0 14.28 0.21301 1.2906 606
+3 0 5.1 0.28066 0.50414 180
+4 0 42.81 0.065425 1.3952 2130
+5 0 10 0.31222 0.67251 215
+6 0 5.88 0.095901 0.27382 286
+7 0 7.27 0.11421 0.39144 343
+8 0 11.11 0.10529 0.40107 381
+9 0 5.26 0.090067 0.27862 309
+10 0 18.18 0.23941 0.64476 269
+11 0 2.61 0.059824 0.20154 337
+12 0 9.67 0.5417 0.8617 159
+13 0 5.55 0.09393 0.30104 320
+14 0 10 0.058626 0.33518 572
+15 0 4.41 0.049205 0.25884 526
+16 0 20 0.24885 0.82579 332
+17 0 7.14 0.14259 0.44406 311
+18 0 9.09 0.18474 0.53112 287
+19 0 18.75 1.6621 1.7755 107
+20 0 18.18 0.085577 0.50977 596
+21 0 11.11 0.80976 1.2008 148
+22 0 17.1 0.1212 1.0258 846
+23 0 5.45 0.10165 0.35029 345
+24 0 12.5 0.094269 0.44264 470
+25 0 20.83 0.5495 1.6713 304
+26 0 16.66 0.26538 0.88696 334
+27 0 33.33 0.7673 3.3673 439
+28 0 9.09 0.12484 0.53858 431
+29 0 14.28 0.098915 0.59333 600
+30 0 5.88 0.10285 0.45668 444
+31 0 12.5 0.064753 0.40339 623
+32 0 4.76 0.047048 0.32856 698
+33 0 18.18 0.097229 0.55591 572
+34 0 4.76 0.047835 0.32945 689
+35 0 20 0.10541 0.53226 505
+36 0 7.69 0.097477 0.40262 413
+37 0 6.89 0.13695 0.42345 309
+38 0 8.33 0.013201 0.22065 1670
+39 0 11.11 0.078629 0.43467 553
+40 0 4.76 0.064834 0.34992 540
+41 0 7.14 0.043667 0.3612 827
+42 0 14.28 0.13234 0.76682 579
+43 0 3.57 0.046099 0.22381 486
+44 0 20 0.079196 0.62198 785
+45 0 21.42 0.30122 1.0117 336
+46 0 22.05 0.17982 0.91112 507
+47 0 2.17 0.0054445 0.076274 1400
+48 0 10 0.031869 0.28573 897
+49 0 4.385 0.038575 0.24347 631
+50 0 9.752 0.13903 0.27036 194
+51 0 4.081 0.016976 0.10939 644
+52 0 32.478 0.26907 0.81567 303
+53 0 6.003 0.075811 0.24588 324
+54 0 19.829 0.044238 0.42934 971
+55 1 1102.5 5.1915 31.729 611
+56 1 9989 52.173 194.89 374
+57 1 15841 283.29 606.35 214
+58 0 1 0.39404 0.4887 124
+
+
+This file: 'spambase.DOCUMENTATION' at the UCI Machine Learning Repository
+http://www.ics.uci.edu/~mlearn/MLRepository.html
|