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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…
754 runs0 likes6 downloads6 reach14 impact
38 instances - 6 features - 2 classes - 0 missing values
analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two…
119 runs0 likes5 downloads5 reach14 impact
50 instances - 6 features - 2 classes - 0 missing values
analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two…
581 runs0 likes5 downloads5 reach14 impact
400 instances - 6 features - 4 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…
708 runs0 likes5 downloads5 reach14 impact
62 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…
1136 runs0 likes8 downloads8 reach14 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…
866 runs1 likes12 downloads13 reach16 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…
631 runs0 likes7 downloads7 reach15 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…
736 runs0 likes5 downloads5 reach14 impact
92 instances - 6 features - 2 classes - 26 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…
789 runs0 likes8 downloads8 reach14 impact
73 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…
780 runs0 likes7 downloads7 reach14 impact
66 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…
1013 runs0 likes8 downloads8 reach15 impact
163 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…
777 runs0 likes8 downloads8 reach15 impact
500 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…
822 runs0 likes7 downloads7 reach15 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…
594 runs0 likes8 downloads8 reach15 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…
853 runs0 likes7 downloads7 reach15 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…
1119 runs0 likes8 downloads8 reach14 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…
759 runs0 likes6 downloads6 reach14 impact
50 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…
757 runs0 likes8 downloads8 reach15 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…
1032 runs0 likes7 downloads7 reach15 impact
151 instances - 6 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 likes20 downloads21 reach27 impact
15545 instances - 6 features - 2 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 reach9 impact
151 instances - 6 features - 3 classes - 0 missing values
1990-05-15 ### BUPA liver disorders 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…
191 runs2 likes30 downloads32 reach11 impact
345 instances - 6 features - 0 classes - 0 missing values
* Dataset Title: Wall-Following Robot Navigation Data Data Set (version with 4 Attributes) * Abstract: The data were collected as the SCITOS G5 robot navigates through the room following the wall in a…
138 runs1 likes7 downloads8 reach15 impact
5456 instances - 5 features - 4 classes - 0 missing values
libSVM","AAD group A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository…
0 runs0 likes0 downloads0 reach16 impact
7089 instances - 5 features - 0 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes2 downloads2 reach9 impact
14 instances - 5 features - 2 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes1 downloads1 reach9 impact
150 instances - 5 features - 3 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes1 downloads1 reach9 impact
14 instances - 5 features - 2 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach9 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach9 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach9 impact
150 instances - 5 features - 3 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes1 downloads1 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes1 downloads1 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach9 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach9 impact
150 instances - 5 features - 3 classes - 0 missing values
test openml upload
0 runs0 likes0 downloads0 reach10 impact
150 instances - 5 features - 3 classes - 0 missing values
iris-example
0 runs0 likes0 downloads0 reach7 impact
150 instances - 5 features - 3 classes - 0 missing values
test
0 runs0 likes0 downloads0 reach6 impact
150 instances - 5 features - classes - 0 missing values
sqs efrf
0 runs0 likes0 downloads0 reach7 impact
4 instances - 5 features - classes - 0 missing values
classification
0 runs0 likes1 downloads1 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs1 likes1 downloads2 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach2 impact
150 instances - 5 features - classes - 0 missing values
This is weather data in arff format
0 runs0 likes0 downloads0 reach2 impact
14 instances - 5 features - 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 likes1 downloads1 reach15 impact
323 instances - 5 features - 0 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
Iris DataSet
0 runs0 likes1 downloads1 reach10 impact
150 instances - 5 features - 3 classes - 0 missing values
Information about the dataset CLASSTYPE: numeric CLASSINDEX: last
2 runs0 likes1 downloads1 reach13 impact
559 instances - 5 features - 0 classes - 0 missing values
Information about the dataset CLASSTYPE: numeric CLASSINDEX: last
2 runs0 likes1 downloads1 reach13 impact
559 instances - 5 features - 0 classes - 0 missing values
A shar archive of data from the book Data Analysis: An Introduction(1992) Prentice Hall bu Jeff Witmer. Submitted by Jeff Witmer (fwitmer@ocvaxa.cc.oberlin.edu) [28/Jun/94] (29 kbytes) Note:…
2 runs0 likes0 downloads0 reach13 impact
50 instances - 5 features - 0 classes - 0 missing values
Dataset from Smoothing Methods in Statistics (ftp stat.cmu.edu/datasets) Simonoff, J.S. (1996). Smoothing Methods in Statistics. New York: Springer-Verlag. Points scored per minute is being treated as…
2 runs0 likes0 downloads0 reach9 impact
96 instances - 5 features - 0 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…
0 runs0 likes0 downloads0 reach8 impact
14 instances - 5 features - 2 classes - 0 missing values
test
0 runs0 likes0 downloads0 reach10 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach10 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach10 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach10 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach8 impact
150 instances - 5 features - classes - 0 missing values
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Identifier attribute deleted. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! NAME: Sexual activity and the lifespan of male fruitflies TYPE: Designed (almost factorial)…
4 runs0 likes2 downloads2 reach12 impact
125 instances - 5 features - 0 classes - 0 missing values
analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two…
0 runs0 likes0 downloads0 reach13 impact
48 instances - 5 features - 0 classes - 0 missing values
1. Title: Employee Selection (Ordinal ESL) 2. Source Informaion: Donor: Arie Ben David MIS, Dept. of Technology Management Holon Academic Inst. of Technology 52 Golomb St. Holon 58102 Israel…
0 runs0 likes0 downloads0 reach13 impact
488 instances - 5 features - 0 classes - 0 missing values
1. Title: Employee Selection (Ordinal ESL) 2. Source Informaion: Donor: Arie Ben David MIS, Dept. of Technology Management Holon Academic Inst. of Technology 52 Golomb St. Holon 58102 Israel…
0 runs0 likes0 downloads0 reach13 impact
488 instances - 5 features - 0 classes - 0 missing values
tmm hjghjg vjgkjhbb nvhjgb
0 runs0 likes0 downloads0 reach8 impact
748 instances - 5 features - classes - 0 missing values
Electrical-Maintenance data set This problem consists of four input variables and the available data set is comprised of a representative number of well distributed examples. In this case, the…
0 runs0 likes0 downloads0 reach8 impact
1056 instances - 5 features - 0 classes - 0 missing values
This data set was originally a univariate time record of a single observed quantity, recorded from a Far-Infrared-Laser in a chaotic state. The original set 1000 points has been adapted for regression…
0 runs0 likes0 downloads0 reach8 impact
993 instances - 5 features - 0 classes - 0 missing values
Data has been taken from various sources such as data gov and various other websites and has been pre processed for analysis purpose
0 runs0 likes0 downloads0 reach7 impact
204 instances - 5 features - classes - 0 missing values
Data originating from the book "Analyzing Categorical Data" by Jeffrey S. Simonoff.
1087 runs0 likes9 downloads9 reach15 impact
50 instances - 5 features - 2 classes - 0 missing values
This dataset is synthetic. It was generated by David Coleman at RCA Laboratories in Princeton, N.J. For convenience, we will refer to it as the POLLEN DATA. The first three variables are the lengths…
0 runs0 likes0 downloads0 reach13 impact
3848 instances - 5 features - 0 classes - 0 missing values
Information about the dataset CLASSTYPE: numeric CLASSINDEX: last
2 runs0 likes0 downloads0 reach13 impact
559 instances - 5 features - 0 classes - 0 missing values
e3r4vr t4r
0 runs0 likes0 downloads0 reach6 impact
2 instances - 5 features - classes - 0 missing values
f fr
0 runs0 likes0 downloads0 reach6 impact
2 instances - 5 features - classes - 0 missing values