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Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
728 runs0 likes7 downloads7 reach8 impact
2000 instances - 241 features - 2 classes - 0 missing values
Jarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in…
440 runs0 likes11 downloads11 reach8 impact
10936 instances - 28 features - 3 classes - 0 missing values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable,…
747 runs0 likes7 downloads7 reach7 impact
145 instances - 95 features - 2 classes - 0 missing values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable,…
109963 runs1 likes19 downloads20 reach20 impact
15545 instances - 6 features - 2 classes - 0 missing values
No data.
747 runs0 likes7 downloads7 reach8 impact
369 instances - 9 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
757 runs0 likes8 downloads8 reach8 impact
400 instances - 6 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
842 runs0 likes7 downloads7 reach8 impact
155 instances - 9 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
676 runs0 likes13 downloads13 reach8 impact
10992 instances - 17 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
794 runs0 likes9 downloads9 reach8 impact
2000 instances - 65 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
765 runs0 likes12 downloads12 reach8 impact
5620 instances - 65 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
766 runs0 likes11 downloads11 reach8 impact
2000 instances - 217 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
778 runs0 likes9 downloads9 reach8 impact
5000 instances - 41 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
169 runs0 likes8 downloads8 reach9 impact
600 instances - 62 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
815 runs0 likes8 downloads8 reach8 impact
336 instances - 8 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
140 runs0 likes6 downloads6 reach8 impact
194 instances - 30 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
732 runs0 likes5 downloads5 reach7 impact
63 instances - 32 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
772 runs0 likes7 downloads7 reach7 impact
214 instances - 10 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
748 runs0 likes8 downloads8 reach7 impact
148 instances - 19 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
1149 runs0 likes10 downloads10 reach7 impact
138 instances - 3 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
774 runs0 likes9 downloads9 reach8 impact
797 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
746 runs0 likes7 downloads7 reach7 impact
72 instances - 4 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
141 runs0 likes7 downloads7 reach7 impact
500 instances - 24 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
700 runs0 likes5 downloads5 reach7 impact
67 instances - 16 features - 2 classes - 0 missing values
Datasets for `Pattern Recognition and Neural Networks' by B.D. Ripley ===================================================================== Cambridge University Press (1996) ISBN 0-521-46086-7 The…
640 runs0 likes6 downloads6 reach7 impact
214 instances - 10 features - 6 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
135 runs0 likes9 downloads9 reach8 impact
3190 instances - 62 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
765 runs0 likes12 downloads12 reach8 impact
1728 instances - 7 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
777 runs0 likes8 downloads8 reach8 impact
625 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
792 runs0 likes8 downloads8 reach8 impact
2000 instances - 48 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
792 runs0 likes10 downloads10 reach8 impact
214 instances - 10 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
173 runs0 likes6 downloads6 reach16 impact
106 instances - 59 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
698 runs0 likes5 downloads5 reach7 impact
36 instances - 23 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
176 runs0 likes7 downloads7 reach7 impact
101 instances - 18 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
801 runs0 likes8 downloads8 reach8 impact
841 instances - 71 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
758 runs0 likes10 downloads10 reach8 impact
2000 instances - 77 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
1139 runs0 likes7 downloads7 reach7 impact
132 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
780 runs0 likes8 downloads8 reach8 impact
178 instances - 14 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
736 runs0 likes7 downloads7 reach8 impact
1473 instances - 10 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
773 runs0 likes8 downloads8 reach8 impact
2000 instances - 7 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
1133 runs0 likes15 downloads15 reach11 impact
150 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
857 runs0 likes12 downloads12 reach10 impact
9961 instances - 15 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
639 runs0 likes12 downloads12 reach8 impact
20000 instances - 17 features - 2 classes - 0 missing values
Dataset from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLVA, GINA, NOVA, HIVA, ADA). The purpose of the challenge…
68261 runs0 likes20 downloads20 reach19 impact
3468 instances - 971 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
772 runs0 likes14 downloads14 reach8 impact
2310 instances - 20 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and…
652 runs0 likes15 downloads15 reach8 impact
12960 instances - 9 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
812 runs0 likes7 downloads7 reach8 impact
559 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
769 runs0 likes7 downloads7 reach8 impact
559 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
779 runs0 likes8 downloads8 reach8 impact
559 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
115 runs0 likes5 downloads5 reach7 impact
40 instances - 3 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
772 runs0 likes7 downloads7 reach8 impact
500 instances - 51 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
617 runs0 likes11 downloads11 reach8 impact
1000 instances - 26 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
817 runs0 likes7 downloads7 reach8 impact
250 instances - 51 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
813 runs0 likes7 downloads7 reach8 impact
662 instances - 4 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
810 runs0 likes6 downloads6 reach7 impact
100 instances - 51 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
653 runs0 likes10 downloads10 reach8 impact
1000 instances - 11 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
154 runs0 likes9 downloads9 reach8 impact
2001 instances - 3 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
842 runs0 likes9 downloads9 reach8 impact
323 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
775 runs0 likes6 downloads6 reach8 impact
500 instances - 26 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
791 runs0 likes6 downloads6 reach8 impact
250 instances - 11 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
797 runs0 likes7 downloads7 reach8 impact
500 instances - 11 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
807 runs0 likes7 downloads7 reach8 impact
500 instances - 51 features - 2 classes - 0 missing values
Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF…
486 runs0 likes14 downloads14 reach9 impact
14395 instances - 109 features - 2 classes - 0 missing values
Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF…
396 runs0 likes16 downloads16 reach8 impact
3468 instances - 785 features - 10 classes - 0 missing values
One of the NASA Metrics Data Program defect data sets. The specific type of software is unknown. Data comes from McCabe and Halstead features extractors of source code. These features were defined in…
777 runs0 likes9 downloads9 reach8 impact
458 instances - 40 features - 2 classes - 0 missing values
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable,…
765 runs0 likes7 downloads7 reach7 impact
145 instances - 95 features - 2 classes - 0 missing values
One of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. Data comes from McCabe and Halstead features extractors of source code. These features…
144599 runs1 likes16 downloads17 reach19 impact
1563 instances - 38 features - 2 classes - 0 missing values
1. Title: Glass Identification Database 2. Sources: (a) Creator: B. German -- Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire RG7 4PN (b) Donor:…
1776 runs0 likes50 downloads50 reach2 impact
214 instances - 10 features - 6 classes - 0 missing values
Primate splice-junction gene sequences (DNA) with associated imperfect domain theory. Splice junctions are points on a DNA sequence at which 'superfluous' DNA is removed during the process of protein…
23161 runs1 likes15 downloads16 reach2 impact
3190 instances - 61 features - 3 classes - 0 missing values
1. Title: Teaching Assistant Evaluation 2. Sources: (a) Collector: Wei-Yin Loh (Department of Statistics, UW-Madison) (b) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) (b) Date: June 7, 1997 3. Past…
2028 runs0 likes13 downloads13 reach2 impact
151 instances - 6 features - 3 classes - 0 missing values
The instances were drawn randomly from a database of 7 outdoor images. The images were hand-segmented to create a classification for every pixel. Each instance is a 3x3 region. ### Attribute…
23139 runs0 likes22 downloads22 reach4 impact
2310 instances - 20 features - 7 classes - 0 missing values
This database encodes the complete set of possible board configurations at the end of tic-tac-toe games, where "x" is assumed to have played first. The target concept is "win for x" (i.e., true when…
385613 runs1 likes66 downloads67 reach2 impact
958 instances - 10 features - 2 classes - 0 missing values
1. Title: Protein Localization Sites 2. Creator and Maintainer: Kenta Nakai Institue of Molecular and Cellular Biology Osaka, University 1-3 Yamada-oka, Suita 565 Japan nakai@imcb.osaka-u.ac.jp…
1803 runs0 likes12 downloads12 reach4 impact
336 instances - 8 features - 8 classes - 0 missing values
NAME: Sonar, Mines vs. Rocks SUMMARY: This is the data set used by Gorman and Sejnowski in their study of the classification of sonar signals using a neural network [1]. The task is to train a network…
2366 runs1 likes25 downloads26 reach2 impact
208 instances - 61 features - 2 classes - 0 missing values
1. Title: Haberman's Survival Data 2. Sources: (a) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) (b) Date: March 4, 1999 3. Past Usage: 1. Haberman, S. J. (1976). Generalized Residuals for Log-Linear…
3241 runs1 likes19 downloads20 reach2 impact
306 instances - 4 features - 2 classes - 0 missing values
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…
158901 runs3 likes83 downloads86 reach4 impact
4601 instances - 58 features - 2 classes - 0 missing values
NAME vehicle silhouettes PURPOSE to classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many…
29018 runs2 likes28 downloads30 reach3 impact
846 instances - 19 features - 4 classes - 0 missing values
Compilation of promoters with known transcriptional start points for E. coli genes. The task is to recognize promoters in strings that represent nucleotides (one of A, G, T, or C). A promoter is a…
138 runs1 likes9 downloads10 reach4 impact
106 instances - 59 features - 2 classes - 0 missing values
This database contains 13 attributes (which have been extracted from a larger set of 75) Attribute Information: ------------------------ -- 1. age -- 2. sex -- 3. chest pain type (4 values) -- 4.…
3214 runs0 likes18 downloads18 reach4 impact
270 instances - 14 features - 2 classes - 0 missing values
A simple database containing 17 Boolean-valued attributes describing animals. The "type" attribute appears to be the class attribute. Notes: * I find it unusual that there are 2 instances of "frog"…
168 runs2 likes17 downloads19 reach2 impact
101 instances - 17 features - 7 classes - 0 missing values
No data.
1038 runs0 likes8 downloads8 reach2 impact
55296 instances - 10 features - 3 classes - 0 missing values
This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See…
2484 runs3 likes27 downloads30 reach4 impact
351 instances - 35 features - 2 classes - 0 missing values
No data.
1457 runs0 likes12 downloads12 reach2 impact
39366 instances - 10 features - 2 classes - 0 missing values
The dataset (originally named ELEC2) contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for…
106352 runs3 likes32 downloads35 reach4 impact
45312 instances - 9 features - 2 classes - 0 missing values
Generator generating 3 classes of waves. Each class is generated from a combination of 2 of 3 "base" waves. For details, see Breiman,L., Friedman,J.H., Olshen,R.A., and Stone,C.J. (1984).…
19675 runs1 likes53 downloads54 reach4 impact
5000 instances - 41 features - 3 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
965 runs0 likes9 downloads9 reach7 impact
137 instances - 8 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
747 runs0 likes12 downloads12 reach8 impact
4177 instances - 9 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
604 runs0 likes9 downloads9 reach8 impact
1000 instances - 26 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
771 runs0 likes9 downloads9 reach8 impact
468 instances - 4 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
747 runs0 likes6 downloads6 reach8 impact
200 instances - 11 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
624 runs0 likes10 downloads10 reach8 impact
15000 instances - 49 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
856 runs0 likes11 downloads11 reach8 impact
209 instances - 7 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
602 runs0 likes12 downloads12 reach8 impact
13750 instances - 41 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
707 runs0 likes6 downloads6 reach7 impact
96 instances - 5 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
748 runs0 likes6 downloads6 reach8 impact
250 instances - 51 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
753 runs0 likes10 downloads10 reach8 impact
8192 instances - 13 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
1190 runs0 likes9 downloads9 reach7 impact
111 instances - 4 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
567 runs0 likes13 downloads13 reach8 impact
40768 instances - 11 features - 2 classes - 0 missing values
Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a…
739 runs0 likes10 downloads10 reach8 impact
4052 instances - 8 features - 2 classes - 0 missing values
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It contains 19 house features plus the price and the id columns,…
0 runs0 likes1 downloads1 reach1 impact
21613 instances - 20 features - 0 classes - 0 missing values
This is a commercial application described in Weiss & Indurkhya (1995). The data describes a telecommunication problem. No further information is available. Characteristics: (10000+5000) cases, 49…
2 runs0 likes4 downloads4 reach2 impact
15000 instances - 49 features - 0 classes - 0 missing values
This data set is also obtained from the task of controlling a F16 aircraft, although the target variable and attributes are different from the ailerons domain. In this case the goal variable is…
2 runs0 likes7 downloads7 reach2 impact
16599 instances - 19 features - 0 classes - 0 missing values