From b4c345e6a5fa929ba20eac19183b9c777055f52d Mon Sep 17 00:00:00 2001 From: Gaspard Coulet Date: Wed, 28 Apr 2021 23:12:36 +0200 Subject: Initial commit --- TrainingSets/bank.data | 1372 ++++++++++++++ TrainingSets/bank.txt | 13 + TrainingSets/cancer.data | 532 ++++++ TrainingSets/cancer.txt | 140 ++ TrainingSets/fire.txt | 66 + TrainingSets/iris.data | 150 ++ TrainingSets/iris.txt | 69 + TrainingSets/spam.data | 4601 ++++++++++++++++++++++++++++++++++++++++++++++ TrainingSets/spam.txt | 90 + 9 files changed, 7033 insertions(+) create mode 100644 TrainingSets/bank.data create mode 100644 TrainingSets/bank.txt create mode 100644 TrainingSets/cancer.data create mode 100644 TrainingSets/cancer.txt create mode 100644 TrainingSets/fire.txt create mode 100644 TrainingSets/iris.data create mode 100644 TrainingSets/iris.txt create mode 100644 TrainingSets/spam.data create mode 100644 TrainingSets/spam.txt (limited to 'TrainingSets') diff --git a/TrainingSets/bank.data b/TrainingSets/bank.data 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+0.40614,1.3492,-1.4501,-0.55949,2 +-1.3887,-4.8773,6.4774,0.34179,2 +-3.7503,-13.4586,17.5932,-2.7771,2 +-3.5637,-8.3827,12.393,-1.2823,2 +-2.5419,-0.65804,2.6842,1.1952,2 diff --git a/TrainingSets/bank.txt b/TrainingSets/bank.txt new file mode 100644 index 0000000..9cae0c7 --- /dev/null +++ b/TrainingSets/bank.txt @@ -0,0 +1,13 @@ +Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) +Source: UCI - 2012 +Please cite: UCI + +Dataset about distinguishing genuine and forged banknotes. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. A Wavelet Transform tool was used to extract features from these images. + +Attribute Information +V1. variance of Wavelet Transformed image (continuous) +V2. skewness of Wavelet Transformed image (continuous) +V3. curtosis of Wavelet Transformed image (continuous) +V4. entropy of image (continuous) + +Class (target). Presumably 1 for genuine and 2 for forged diff --git a/TrainingSets/cancer.data b/TrainingSets/cancer.data new file mode 100644 index 0000000..618fc2c --- /dev/null +++ b/TrainingSets/cancer.data @@ -0,0 +1,532 @@ +5,1,1,1,2,1,3,1,1,0 +5,4,4,5,7,10,3,2,1,0 +3,1,1,1,2,2,3,1,1,0 +6,8,8,1,3,4,3,7,1,0 +4,1,1,3,2,1,3,1,1,0 +8,10,10,8,7,10,9,7,1,1 +1,1,1,1,2,10,3,1,1,0 +2,1,2,1,2,1,3,1,1,0 +2,1,1,1,2,1,1,1,5,0 +4,2,1,1,2,1,2,1,1,0 +1,1,1,1,1,1,3,1,1,0 +2,1,1,1,2,1,2,1,1,0 +5,3,3,3,2,3,4,4,1,1 +1,1,1,1,2,3,3,1,1,0 +8,7,5,10,7,9,5,5,4,1 +7,4,6,4,6,1,4,3,1,1 +4,1,1,1,2,1,2,1,1,0 +4,1,1,1,2,1,3,1,1,0 +10,7,7,6,4,10,4,1,2,1 +6,1,1,1,2,1,3,1,1,0 +7,3,2,10,5,10,5,4,4,1 +10,5,5,3,6,7,7,10,1,1 +3,1,1,1,2,1,2,1,1,0 +8,4,5,1,2,3.544656,7,3,1,1 +1,1,1,1,2,1,3,1,1,0 +5,2,3,4,2,7,3,6,1,1 +3,2,1,1,1,1,2,1,1,0 +5,1,1,1,2,1,2,1,1,0 +2,1,1,1,2,1,2,1,1,0 +1,1,3,1,2,1,1,1,1,0 +3,1,1,1,1,1,2,1,1,0 +2,1,1,1,2,1,3,1,1,0 +10,7,7,3,8,5,7,4,3,1 +2,1,1,2,2,1,3,1,1,0 +3,1,2,1,2,1,2,1,1,0 +2,1,1,1,2,1,2,1,1,0 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+4,2,2,1,2,1,2,1,1,0 diff --git a/TrainingSets/cancer.txt b/TrainingSets/cancer.txt new file mode 100644 index 0000000..ed45dae --- /dev/null +++ b/TrainingSets/cancer.txt @@ -0,0 +1,140 @@ +1. Title: Wisconsin Diagnostic Breast Cancer (WDBC) + +2. Source Information + +a) Creators: + + Dr. William H. Wolberg, General Surgery Dept., University of + Wisconsin, Clinical Sciences Center, Madison, WI 53792 + wolberg@eagle.surgery.wisc.edu + + W. Nick Street, Computer Sciences Dept., University of + Wisconsin, 1210 West Dayton St., Madison, WI 53706 + street@cs.wisc.edu 608-262-6619 + + Olvi L. Mangasarian, Computer Sciences Dept., University of + Wisconsin, 1210 West Dayton St., Madison, WI 53706 + olvi@cs.wisc.edu + +b) Donor: Nick Street + +c) Date: November 1995 + +3. Past Usage: + +first usage: + + W.N. Street, W.H. Wolberg and O.L. Mangasarian + Nuclear feature extraction for breast tumor diagnosis. + IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science + and Technology, volume 1905, pages 861-870, San Jose, CA, 1993. + +OR literature: + + O.L. Mangasarian, W.N. Street and W.H. Wolberg. + Breast cancer diagnosis and prognosis via linear programming. + Operations Research, 43(4), pages 570-577, July-August 1995. + +Medical literature: + + W.H. Wolberg, W.N. Street, and O.L. Mangasarian. + Machine learning techniques to diagnose breast cancer from + fine-needle aspirates. + Cancer Letters 77 (1994) 163-171. + + W.H. Wolberg, W.N. Street, and O.L. Mangasarian. + Image analysis and machine learning applied to breast cancer + diagnosis and prognosis. + Analytical and Quantitative Cytology and Histology, Vol. 17 + No. 2, pages 77-87, April 1995. + + W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. + Computerized breast cancer diagnosis and prognosis from fine + needle aspirates. + Archives of Surgery 1995;130:511-516. + + W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. + Computer-derived nuclear features distinguish malignant from + benign breast cytology. + Human Pathology, 26:792--796, 1995. + +See also: + http://www.cs.wisc.edu/~olvi/uwmp/mpml.html + http://www.cs.wisc.edu/~olvi/uwmp/cancer.html + +Results: + + - predicting field 2, diagnosis: B = benign, M = malignant + - sets are linearly separable using all 30 input features + - best predictive accuracy obtained using one separating plane + in the 3-D space of Worst Area, Worst Smoothness and + Mean Texture. Estimated accuracy 97.5% using repeated + 10-fold crossvalidations. Classifier has correctly + diagnosed 176 consecutive new patients as of November + 1995. + +4. Relevant information + + Features are computed from a digitized image of a fine needle + aspirate (FNA) of a breast mass. They describe + characteristics of the cell nuclei present in the image. + A few of the images can be found at + http://www.cs.wisc.edu/~street/images/ + + Separating plane described above was obtained using + Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree + Construction Via Linear Programming." Proceedings of the 4th + Midwest Artificial Intelligence and Cognitive Science Society, + pp. 97-101, 1992], a classification method which uses linear + programming to construct a decision tree. Relevant features + were selected using an exhaustive search in the space of 1-4 + features and 1-3 separating planes. + + The actual linear program used to obtain the separating plane + in the 3-dimensional space is that described in: + [K. P. Bennett and O. L. Mangasarian: "Robust Linear + Programming Discrimination of Two Linearly Inseparable Sets", + Optimization Methods and Software 1, 1992, 23-34]. + + + This database is also available through the UW CS ftp server: + + ftp ftp.cs.wisc.edu + cd math-prog/cpo-dataset/machine-learn/WDBC/ + +5. Number of instances: 569 + +6. Number of attributes: 32 (ID, diagnosis, 30 real-valued input features) + +7. Attribute information + +1) ID number +2) Diagnosis (M = malignant, B = benign) +3-32) + +Ten real-valued features are computed for each cell nucleus: + + a) radius (mean of distances from center to points on the perimeter) + b) texture (standard deviation of gray-scale values) + c) perimeter + d) area + e) smoothness (local variation in radius lengths) + f) compactness (perimeter^2 / area - 1.0) + g) concavity (severity of concave portions of the contour) + h) concave points (number of concave portions of the contour) + i) symmetry + j) fractal dimension ("coastline approximation" - 1) + +Several of the papers listed above contain detailed descriptions of +how these features are computed. + +The mean, standard error, and "worst" or largest (mean of the three +largest values) of these features were computed for each image, +resulting in 30 features. For instance, field 3 is Mean Radius, field +13 is Radius SE, field 23 is Worst Radius. + +All feature values are recoded with four significant digits. + +8. Missing attribute values: none + +9. Class distribution: 357 benign, 212 malignant diff --git a/TrainingSets/fire.txt b/TrainingSets/fire.txt new file mode 100644 index 0000000..c6de15b --- /dev/null +++ b/TrainingSets/fire.txt @@ -0,0 +1,66 @@ +Citation Request: + This dataset is public available for research. The details are described in [Cortez and Morais, 2007]. + Please include this citation if you plan to use this database: + + P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. + In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, + Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, + Guimaraes, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9. + Available at: http://www.dsi.uminho.pt/~pcortez/fires.pdf + +1. Title: Forest Fires + +2. Sources + Created by: Paulo Cortez and An�bal Morais (Univ. Minho) @ 2007 + +3. Past Usage: + + P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. + In Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, + December, 2007. (http://www.dsi.uminho.pt/~pcortez/fires.pdf) + + In the above reference, the output "area" was first transformed with a ln(x+1) function. + Then, several Data Mining methods were applied. After fitting the models, the outputs were + post-processed with the inverse of the ln(x+1) transform. Four different input setups were + used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two + regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed + with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: + 12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The + best RMSE was attained by the naive mean predictor. An analysis to the regression error curve + (REC) shows that the SVM model predicts more examples within a lower admitted error. In effect, + the SVM model predicts better small fires, which are the majority. + +4. Relevant Information: + + This is a very difficult regression task. It can be used to test regression methods. Also, + it could be used to test outlier detection methods, since it is not clear how many outliers + are there. Yet, the number of examples of fires with a large burned area is very small. + +5. Number of Instances: 517 + +6. Number of Attributes: 12 + output attribute + + Note: several of the attributes may be correlated, thus it makes sense to apply some sort of + feature selection. + +7. Attribute information: + + For more information, read [Cortez and Morais, 2007]. + + 1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9 + 2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9 + 3. month - month of the year: "jan" to "dec" + 4. day - day of the week: "mon" to "sun" + 5. FFMC - FFMC index from the FWI system: 18.7 to 96.20 + 6. DMC - DMC index from the FWI system: 1.1 to 291.3 + 7. DC - DC index from the FWI system: 7.9 to 860.6 + 8. ISI - ISI index from the FWI system: 0.0 to 56.10 + 9. temp - temperature in Celsius degrees: 2.2 to 33.30 + 10. RH - relative humidity in %: 15.0 to 100 + 11. wind - wind speed in km/h: 0.40 to 9.40 + 12. rain - outside rain in mm/m2 : 0.0 to 6.4 + 13. area - the burned area of the forest (in ha): 0.00 to 1090.84 + (this output variable is very skewed towards 0.0, thus it may make + sense to model with the logarithm transform). + +8. Missing Attribute Values: None diff --git a/TrainingSets/iris.data b/TrainingSets/iris.data new file mode 100644 index 0000000..02360f0 --- /dev/null +++ b/TrainingSets/iris.data @@ -0,0 +1,150 @@ +5.1,3.5,1.4,0.2,0 +4.9,3.0,1.4,0.2,0 +4.7,3.2,1.3,0.2,0 +4.6,3.1,1.5,0.2,0 +5.0,3.6,1.4,0.2,0 +5.4,3.9,1.7,0.4,0 +4.6,3.4,1.4,0.3,0 +5.0,3.4,1.5,0.2,0 +4.4,2.9,1.4,0.2,0 +4.9,3.1,1.5,0.1,0 +5.4,3.7,1.5,0.2,0 +4.8,3.4,1.6,0.2,0 +4.8,3.0,1.4,0.1,0 +4.3,3.0,1.1,0.1,0 +5.8,4.0,1.2,0.2,0 +5.7,4.4,1.5,0.4,0 +5.4,3.9,1.3,0.4,0 +5.1,3.5,1.4,0.3,0 +5.7,3.8,1.7,0.3,0 +5.1,3.8,1.5,0.3,0 +5.4,3.4,1.7,0.2,0 +5.1,3.7,1.5,0.4,0 +4.6,3.6,1.0,0.2,0 +5.1,3.3,1.7,0.5,0 +4.8,3.4,1.9,0.2,0 +5.0,3.0,1.6,0.2,0 +5.0,3.4,1.6,0.4,0 +5.2,3.5,1.5,0.2,0 +5.2,3.4,1.4,0.2,0 +4.7,3.2,1.6,0.2,0 +4.8,3.1,1.6,0.2,0 +5.4,3.4,1.5,0.4,0 +5.2,4.1,1.5,0.1,0 +5.5,4.2,1.4,0.2,0 +4.9,3.1,1.5,0.1,0 +5.0,3.2,1.2,0.2,0 +5.5,3.5,1.3,0.2,0 +4.9,3.1,1.5,0.1,0 +4.4,3.0,1.3,0.2,0 +5.1,3.4,1.5,0.2,0 +5.0,3.5,1.3,0.3,0 +4.5,2.3,1.3,0.3,0 +4.4,3.2,1.3,0.2,0 +5.0,3.5,1.6,0.6,0 +5.1,3.8,1.9,0.4,0 +4.8,3.0,1.4,0.3,0 +5.1,3.8,1.6,0.2,0 +4.6,3.2,1.4,0.2,0 +5.3,3.7,1.5,0.2,0 +5.0,3.3,1.4,0.2,0 +7.0,3.2,4.7,1.4,1.0 +6.4,3.2,4.5,1.5,1.0 +6.9,3.1,4.9,1.5,1.0 +5.5,2.3,4.0,1.3,1.0 +6.5,2.8,4.6,1.5,1.0 +5.7,2.8,4.5,1.3,1.0 +6.3,3.3,4.7,1.6,1.0 +4.9,2.4,3.3,1.0,1.0 +6.6,2.9,4.6,1.3,1.0 +5.2,2.7,3.9,1.4,1.0 +5.0,2.0,3.5,1.0,1.0 +5.9,3.0,4.2,1.5,1.0 +6.0,2.2,4.0,1.0,1.0 +6.1,2.9,4.7,1.4,1.0 +5.6,2.9,3.6,1.3,1.0 +6.7,3.1,4.4,1.4,1.0 +5.6,3.0,4.5,1.5,1.0 +5.8,2.7,4.1,1.0,1.0 +6.2,2.2,4.5,1.5,1.0 +5.6,2.5,3.9,1.1,1.0 +5.9,3.2,4.8,1.8,1.0 +6.1,2.8,4.0,1.3,1.0 +6.3,2.5,4.9,1.5,1.0 +6.1,2.8,4.7,1.2,1.0 +6.4,2.9,4.3,1.3,1.0 +6.6,3.0,4.4,1.4,1.0 +6.8,2.8,4.8,1.4,1.0 +6.7,3.0,5.0,1.7,1.0 +6.0,2.9,4.5,1.5,1.0 +5.7,2.6,3.5,1.0,1.0 +5.5,2.4,3.8,1.1,1.0 +5.5,2.4,3.7,1.0,1.0 +5.8,2.7,3.9,1.2,1.0 +6.0,2.7,5.1,1.6,1.0 +5.4,3.0,4.5,1.5,1.0 +6.0,3.4,4.5,1.6,1.0 +6.7,3.1,4.7,1.5,1.0 +6.3,2.3,4.4,1.3,1.0 +5.6,3.0,4.1,1.3,1.0 +5.5,2.5,4.0,1.3,1.0 +5.5,2.6,4.4,1.2,1.0 +6.1,3.0,4.6,1.4,1.0 +5.8,2.6,4.0,1.2,1.0 +5.0,2.3,3.3,1.0,1.0 +5.6,2.7,4.2,1.3,1.0 +5.7,3.0,4.2,1.2,1.0 +5.7,2.9,4.2,1.3,1.0 +6.2,2.9,4.3,1.3,1.0 +5.1,2.5,3.0,1.1,1.0 +5.7,2.8,4.1,1.3,1.0 +6.3,3.3,6.0,2.5,2.0 +5.8,2.7,5.1,1.9,2.0 +7.1,3.0,5.9,2.1,2.0 +6.3,2.9,5.6,1.8,2.0 +6.5,3.0,5.8,2.2,2.0 +7.6,3.0,6.6,2.1,2.0 +4.9,2.5,4.5,1.7,2.0 +7.3,2.9,6.3,1.8,2.0 +6.7,2.5,5.8,1.8,2.0 +7.2,3.6,6.1,2.5,2.0 +6.5,3.2,5.1,2.0,2.0 +6.4,2.7,5.3,1.9,2.0 +6.8,3.0,5.5,2.1,2.0 +5.7,2.5,5.0,2.0,2.0 +5.8,2.8,5.1,2.4,2.0 +6.4,3.2,5.3,2.3,2.0 +6.5,3.0,5.5,1.8,2.0 +7.7,3.8,6.7,2.2,2.0 +7.7,2.6,6.9,2.3,2.0 +6.0,2.2,5.0,1.5,2.0 +6.9,3.2,5.7,2.3,2.0 +5.6,2.8,4.9,2.0,2.0 +7.7,2.8,6.7,2.0,2.0 +6.3,2.7,4.9,1.8,2.0 +6.7,3.3,5.7,2.1,2.0 +7.2,3.2,6.0,1.8,2.0 +6.2,2.8,4.8,1.8,2.0 +6.1,3.0,4.9,1.8,2.0 +6.4,2.8,5.6,2.1,2.0 +7.2,3.0,5.8,1.6,2.0 +7.4,2.8,6.1,1.9,2.0 +7.9,3.8,6.4,2.0,2.0 +6.4,2.8,5.6,2.2,2.0 +6.3,2.8,5.1,1.5,2.0 +6.1,2.6,5.6,1.4,2.0 +7.7,3.0,6.1,2.3,2.0 +6.3,3.4,5.6,2.4,2.0 +6.4,3.1,5.5,1.8,2.0 +6.0,3.0,4.8,1.8,2.0 +6.9,3.1,5.4,2.1,2.0 +6.7,3.1,5.6,2.4,2.0 +6.9,3.1,5.1,2.3,2.0 +5.8,2.7,5.1,1.9,2.0 +6.8,3.2,5.9,2.3,2.0 +6.7,3.3,5.7,2.5,2.0 +6.7,3.0,5.2,2.3,2.0 +6.3,2.5,5.0,1.9,2.0 +6.5,3.0,5.2,2.0,2.0 +6.2,3.4,5.4,2.3,2.0 +5.9,3.0,5.1,1.8,2.0 diff --git a/TrainingSets/iris.txt b/TrainingSets/iris.txt new file mode 100644 index 0000000..388de27 --- /dev/null +++ b/TrainingSets/iris.txt @@ -0,0 +1,69 @@ +1. Title: Iris Plants Database + Updated Sept 21 by C.Blake - Added discrepency information + +2. Sources: + (a) Creator: R.A. Fisher + (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) + (c) Date: July, 1988 + +3. Past Usage: + - Publications: too many to mention!!! Here are a few. + 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems" + Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions + to Mathematical Statistics" (John Wiley, NY, 1950). + 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. + (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. + 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System + Structure and Classification Rule for Recognition in Partially Exposed + Environments". IEEE Transactions on Pattern Analysis and Machine + Intelligence, Vol. PAMI-2, No. 1, 67-71. + -- Results: + -- very low misclassification rates (0% for the setosa class) + 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE + Transactions on Information Theory, May 1972, 431-433. + -- Results: + -- very low misclassification rates again + 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II + conceptual clustering system finds 3 classes in the data. + +4. Relevant Information: + --- This is perhaps the best known database to be found in the pattern + recognition literature. Fisher's paper is a classic in the field + and is referenced frequently to this day. (See Duda & Hart, for + example.) The data set contains 3 classes of 50 instances each, + where each class refers to a type of iris plant. One class is + linearly separable from the other 2; the latter are NOT linearly + separable from each other. + --- Predicted attribute: class of iris plant. + --- This is an exceedingly simple domain. + --- This data differs from the data presented in Fishers article + (identified by Steve Chadwick, spchadwick@espeedaz.net ) + The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" + where the error is in the fourth feature. + The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" + where the errors are in the second and third features. + +5. Number of Instances: 150 (50 in each of three classes) + +6. Number of Attributes: 4 numeric, predictive attributes and the class + +7. Attribute Information: + 1. sepal length in cm + 2. sepal width in cm + 3. petal length in cm + 4. petal width in cm + 5. class: + -- Iris Setosa + -- Iris Versicolour + -- Iris Virginica + +8. Missing Attribute Values: None + +Summary Statistics: + Min Max Mean SD Class Correlation + sepal length: 4.3 7.9 5.84 0.83 0.7826 + sepal width: 2.0 4.4 3.05 0.43 -0.4194 + petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) + petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) + +9. Class Distribution: 33.3% for each of 3 classes. diff --git a/TrainingSets/spam.data b/TrainingSets/spam.data new file mode 100644 index 0000000..b6dd6cc --- /dev/null +++ b/TrainingSets/spam.data @@ -0,0 +1,4601 @@ +0,0.64,0.64,0,0.32,0,0,0,0,0,0,0.64,0,0,0,0.32,0,1.29,1.93,0,0.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.778,0,0,3.756,61,278,1 +0.21,0.28,0.5,0,0.14,0.28,0.21,0.07,0,0.94,0.21,0.79,0.65,0.21,0.14,0.14,0.07,0.28,3.47,0,1.59,0,0.43,0.43,0,0,0,0,0,0,0,0,0,0,0,0,0.07,0,0,0,0,0,0,0,0,0,0,0,0,0.132,0,0.372,0.18,0.048,5.114,101,1028,1 +0.06,0,0.71,0,1.23,0.19,0.19,0.12,0.64,0.25,0.38,0.45,0.12,0,1.75,0.06,0.06,1.03,1.36,0.32,0.51,0,1.16,0.06,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0,0,0.12,0,0.06,0.06,0,0,0.01,0.143,0,0.276,0.184,0.01,9.821,485,2259,1 +0,0,0,0,0.63,0,0.31,0.63,0.31,0.63,0.31,0.31,0.31,0,0,0.31,0,0,3.18,0,0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.137,0,0.137,0,0,3.537,40,191,1 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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. +| +| For more information, see file 'spambase.DOCUMENTATION' at the +| UCI Machine Learning Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html + + +1, 0. | spam, non-spam classes + +word_freq_make: continuous. +word_freq_address: continuous. +word_freq_all: continuous. +word_freq_3d: continuous. +word_freq_our: continuous. +word_freq_over: continuous. +word_freq_remove: continuous. +word_freq_internet: continuous. +word_freq_order: continuous. +word_freq_mail: continuous. +word_freq_receive: continuous. +word_freq_will: continuous. +word_freq_people: continuous. +word_freq_report: continuous. +word_freq_addresses: continuous. +word_freq_free: continuous. +word_freq_business: continuous. +word_freq_email: continuous. +word_freq_you: continuous. +word_freq_credit: continuous. +word_freq_your: continuous. +word_freq_font: continuous. +word_freq_000: continuous. +word_freq_money: continuous. +word_freq_hp: continuous. +word_freq_hpl: continuous. +word_freq_george: continuous. +word_freq_650: continuous. +word_freq_lab: continuous. +word_freq_labs: continuous. +word_freq_telnet: continuous. +word_freq_857: continuous. +word_freq_data: continuous. +word_freq_415: continuous. +word_freq_85: continuous. +word_freq_technology: continuous. +word_freq_1999: continuous. +word_freq_parts: continuous. +word_freq_pm: continuous. +word_freq_direct: continuous. +word_freq_cs: continuous. +word_freq_meeting: continuous. +word_freq_original: continuous. +word_freq_project: continuous. +word_freq_re: continuous. +word_freq_edu: continuous. +word_freq_table: continuous. +word_freq_conference: continuous. +char_freq_;: continuous. +char_freq_(: continuous. +char_freq_[: continuous. +char_freq_!: continuous. +char_freq_$: continuous. +char_freq_#: continuous. +capital_run_length_average: continuous. +capital_run_length_longest: continuous. +capital_run_length_total: continuous. -- cgit v1.2.3