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Citation Request: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data.…
1973 runs0 likes0 downloads0 reach0 impact
148 instances - 19 features - 4 classes - 0 missing values
One of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same…
38885 runs0 likes0 downloads0 reach0 impact
2000 instances - 65 features - 10 classes - 0 missing values
Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data.…
2009 runs0 likes0 downloads0 reach0 impact
286 instances - 10 features - 2 classes - 9 missing values
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 9 features - 2 classes - 0 missing values
See [https://github.com/slds-lmu/paper_2023_ci_for_ge](https://github.com/slds-lmu/paper_2023_ci_for_ge) for a description.
0 runs0 likes0 downloads0 reach0 impact
5100000 instances - 15 features - 0 classes - 0 missing values
One of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. Corresponding patterns in different datasets correspond to the same…
38439 runs0 likes0 downloads0 reach0 impact
2000 instances - 77 features - 10 classes - 0 missing values
The first 5 variables are all blood tests which are thought to be sensitive to liver disorders that might arise from excessive alcohol consumption. Each line in the dataset constitutes the record of a…
238 runs0 likes0 downloads0 reach0 impact
345 instances - 6 features - 0 classes - 0 missing values
Dataset created to study concept drift in stream mining. It is constructed by combining the Covertype, Poker-Hand, and Electricity datasets. More details can be found in: Albert Bifet, Geoff Holmes,…
332 runs0 likes0 downloads0 reach0 impact
1455525 instances - 73 features - 10 classes - 0 missing values
Normalized version of the Forest Covertype dataset (see version 1), so that the numerical values are between 0 and 1. Contains the forest cover type for 30 x 30 meter cells obtained from US Forest…
342 runs0 likes0 downloads0 reach0 impact
581012 instances - 55 features - 7 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…
107182 runs0 likes0 downloads0 reach0 impact
45312 instances - 9 features - 2 classes - 0 missing values
Synthetic dataset. Almost identical to [dataset 152](https://www.openml.org/d/153/edit)
319 runs0 likes0 downloads0 reach0 impact
1000000 instances - 11 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).…
20129 runs0 likes0 downloads0 reach0 impact
5000 instances - 41 features - 3 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
500 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
250 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
1000 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
500 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes1 downloads1 reach13 impact
1000 instances - 51 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
1 runs0 likes0 downloads0 reach0 impact
500 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes1 downloads1 reach13 impact
1000 instances - 51 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
100 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes1 downloads1 reach13 impact
1000 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
100 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes3 downloads3 reach14 impact
1000 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
100 instances - 51 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
1000 instances - 6 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
100 instances - 51 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
250 instances - 6 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
250 instances - 6 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
500 instances - 6 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes1 downloads1 reach13 impact
1000 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
1000 instances - 101 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes2 downloads2 reach13 impact
1000 instances - 26 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
1000 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
100 instances - 6 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
1 runs0 likes0 downloads0 reach0 impact
1000 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
250 instances - 11 features - 0 classes - 0 missing values
The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as…
0 runs0 likes0 downloads0 reach0 impact
250 instances - 51 features - 0 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 likes0 downloads0 reach0 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…
753 runs0 likes0 downloads0 reach0 impact
508 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…
600 runs0 likes0 downloads0 reach0 impact
1000 instances - 101 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…
965 runs0 likes0 downloads0 reach0 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…
771 runs0 likes0 downloads0 reach0 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…
761 runs0 likes0 downloads0 reach0 impact
8192 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…
1111 runs0 likes0 downloads0 reach0 impact
100 instances - 6 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…
1043 runs0 likes0 downloads0 reach0 impact
125 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…
608 runs0 likes9 downloads9 reach15 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…
766 runs0 likes0 downloads0 reach0 impact
100 instances - 51 features - 2 classes - 0 missing values
This S dump contains 22 data sets from the book Visualizing Data published by Hobart Press (books@hobart.com). The dump was created by data.dump() and can be read back into S by data.restore(). The…
0 runs0 likes0 downloads0 reach0 impact
88 instances - 3 features - 0 classes - 0 missing values
File README ----------- chscase A collection of the data sets used in the book "A Casebook for a First Course in Statistics and Data Analysis," by Samprit Chatterjee, Mark S. Handcock and Jeffrey S.…
0 runs0 likes0 downloads0 reach0 impact
222 instances - 3 features - 0 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…
111 runs0 likes0 downloads0 reach0 impact
52 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…
567 runs0 likes0 downloads0 reach0 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 likes0 downloads0 reach0 impact
4052 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…
703 runs0 likes0 downloads0 reach0 impact
44 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…
624 runs0 likes0 downloads0 reach0 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…
604 runs0 likes9 downloads9 reach15 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…
854 runs0 likes0 downloads0 reach0 impact
250 instances - 6 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 likes0 downloads0 reach0 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…
747 runs0 likes0 downloads0 reach0 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…
726 runs0 likes0 downloads0 reach0 impact
576 instances - 12 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 likes0 downloads0 reach0 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…
734 runs0 likes0 downloads0 reach0 impact
100 instances - 101 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…
581 runs0 likes0 downloads0 reach0 impact
20640 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…
773 runs0 likes0 downloads0 reach0 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…
720 runs0 likes0 downloads0 reach0 impact
506 instances - 21 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…
1119 runs0 likes0 downloads0 reach0 impact
100 instances - 6 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…
782 runs0 likes0 downloads0 reach0 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…
621 runs0 likes8 downloads8 reach15 impact
1000 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…
796 runs0 likes0 downloads0 reach0 impact
8192 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…
970 runs0 likes0 downloads0 reach0 impact
100 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…
866 runs0 likes0 downloads0 reach0 impact
7129 instances - 6 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…
782 runs0 likes0 downloads0 reach0 impact
70 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…
784 runs0 likes0 downloads0 reach0 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…
723 runs0 likes0 downloads0 reach0 impact
418 instances - 19 features - 2 classes - 1239 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…
1059 runs0 likes0 downloads0 reach0 impact
264 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…
776 runs0 likes0 downloads0 reach0 impact
100 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…
598 runs0 likes0 downloads0 reach0 impact
1000 instances - 6 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…
903 runs0 likes0 downloads0 reach0 impact
468 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…
728 runs0 likes0 downloads0 reach0 impact
52 instances - 10 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…
761 runs0 likes0 downloads0 reach0 impact
8192 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…
752 runs0 likes0 downloads0 reach0 impact
48 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…
756 runs0 likes0 downloads0 reach0 impact
310 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…
774 runs0 likes0 downloads0 reach0 impact
9517 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…
810 runs0 likes0 downloads0 reach0 impact
235 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…
604 runs0 likes0 downloads0 reach0 impact
22784 instances - 17 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…
102 runs0 likes0 downloads0 reach0 impact
527 instances - 37 features - 2 classes - 542 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…
767 runs0 likes0 downloads0 reach0 impact
189 instances - 10 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 likes0 downloads0 reach0 impact
559 instances - 5 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 likes0 downloads0 reach0 impact
214 instances - 10 features - 6 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 likes0 downloads0 reach0 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 likes0 downloads0 reach0 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…
806 runs0 likes0 downloads0 reach0 impact
250 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…
807 runs0 likes0 downloads0 reach0 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…
112 runs0 likes0 downloads0 reach0 impact
42 instances - 10 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…
118 runs0 likes0 downloads0 reach0 impact
228 instances - 9 features - 2 classes - 20 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…
119 runs0 likes0 downloads0 reach0 impact
50 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…
814 runs0 likes0 downloads0 reach0 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…
744 runs0 likes0 downloads0 reach0 impact
130 instances - 10 features - 2 classes - 97 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…
1899 runs0 likes0 downloads0 reach0 impact
1156 instances - 6 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 likes0 downloads0 reach0 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 likes0 downloads0 reach0 impact
500 instances - 11 features - 2 classes - 0 missing values