Data
Filter results by:
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 reach0 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 reach0 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 reach0 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 reach1 impact
14 instances - 5 features - 2 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach1 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach1 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach1 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach1 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach1 impact
150 instances - 5 features - classes - 0 missing values
The dataset collects data from an Android smartphone positioned in the chest pocket. Accelerometer Data are collected from 22 participants walking in the wild over a predefined path. The dataset is…
81 runs0 likes7 downloads7 reach6 impact
149332 instances - 5 features - 22 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 likes6 downloads7 reach7 impact
5456 instances - 5 features - 4 classes - 0 missing values
test
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
test
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 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 reach0 impact
14 instances - 5 features - 2 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 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 reach1 impact
14 instances - 5 features - 2 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 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 reach1 impact
125 instances - 5 features - 0 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes1 downloads1 reach1 impact
150 instances - 5 features - 3 classes - 0 missing values
classification
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - 3 classes - 0 missing values
iris with ignored features Sepal.Width and Petal.Length
0 runs0 likes0 downloads0 reach0 impact
150 instances - 5 features - classes - 0 missing values
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…
8057 runs7 likes106 downloads113 reach3 impact
150 instances - 5 features - 3 classes - 0 missing values
Hayes-Roth Database This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of the possible tasks. Source…
380 runs0 likes3 downloads3 reach16 impact
160 instances - 5 features - 3 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…
886 runs0 likes8 downloads8 reach7 impact
264 instances - 5 features - 2 classes - 0 missing values
One of the datasets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff. It contains data on the DMFT Index (Decayed, Missing, and Filled Teeth) before and after different prevention…
26657 runs0 likes11 downloads11 reach34 impact
797 instances - 5 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…
1041 runs0 likes10 downloads10 reach6 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…
705 runs0 likes6 downloads6 reach6 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…
1058 runs0 likes9 downloads9 reach7 impact
167 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…
453 runs0 likes5 downloads5 reach6 impact
108 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…
707 runs0 likes9 downloads9 reach6 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…
750 runs0 likes5 downloads5 reach6 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…
779 runs0 likes8 downloads8 reach7 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…
812 runs0 likes7 downloads7 reach7 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 reach7 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…
117 runs0 likes7 downloads7 reach6 impact
50 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…
774 runs0 likes9 downloads9 reach7 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…
842 runs0 likes9 downloads9 reach7 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…
773 runs0 likes11 downloads11 reach7 impact
8641 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…
121 runs0 likes6 downloads6 reach6 impact
46 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…
114 runs0 likes5 downloads5 reach6 impact
70 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…
1137 runs0 likes7 downloads7 reach6 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…
777 runs0 likes8 downloads8 reach7 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…
1133 runs0 likes15 downloads15 reach10 impact
150 instances - 5 features - 2 classes - 0 missing values
This data set was generated to model psychological experimental results. Each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. The attributes are…
26472 runs2 likes15 downloads17 reach3 impact
625 instances - 5 features - 3 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 reach7 impact
797 instances - 5 features - 2 classes - 0 missing values
Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/banknote+authentication) - 2012 Please cite:…
134842 runs1 likes22 downloads23 reach21 impact
1372 instances - 5 features - 2 classes - 0 missing values
Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem. To demonstrate the RFMTC marketing model (a modified version of RFM), this study…
464088 runs3 likes58 downloads61 reach27 impact
748 instances - 5 features - 2 classes - 0 missing values
YAGO Schema.
0 runs0 likes0 downloads0 reach3 impact
181 instances - 4 features - 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.
2 runs0 likes0 downloads0 reach1 impact
52 instances - 4 features - 0 classes - 0 missing values
No data.
328 runs0 likes3 downloads3 reach2 impact
1000000 instances - 4 features - 2 classes - 0 missing values
No data.
330 runs0 likes5 downloads5 reach2 impact
1000000 instances - 4 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…
0 runs0 likes1 downloads1 reach5 impact
365 instances - 4 features - 0 classes - 30 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…
2 runs0 likes0 downloads0 reach5 impact
450 instances - 4 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…
2 runs0 likes0 downloads0 reach5 impact
475 instances - 4 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…
2 runs0 likes0 downloads0 reach5 impact
475 instances - 4 features - 0 classes - 0 missing values
Data Used in "A BAYESIAN APPROACH TO DATA DISCLOSURE: OPTIMAL INTRUDER BEHAVIOR FOR CONTINUOUS DATA" by Stephen E. Fienberg, Udi E. Makov, and Ashish P. Sanil Background: ========== In this paper we…
0 runs0 likes0 downloads0 reach5 impact
662 instances - 4 features - 0 classes - 0 missing values
Data Used in "A BAYESIAN APPROACH TO DATA DISCLOSURE: OPTIMAL INTRUDER BEHAVIOR FOR CONTINUOUS DATA" by Stephen E. Fienberg, Udi E. Makov, and Ashish P. Sanil Background: ========== In this paper we…
0 runs0 likes0 downloads0 reach5 impact
662 instances - 4 features - 0 classes - 0 missing values
Data Used in "A BAYESIAN APPROACH TO DATA DISCLOSURE: OPTIMAL INTRUDER BEHAVIOR FOR CONTINUOUS DATA" by Stephen E. Fienberg, Udi E. Makov, and Ashish P. Sanil Background: ========== In this paper we…
0 runs0 likes1 downloads1 reach5 impact
662 instances - 4 features - 0 classes - 0 missing values
No data.
307 runs0 likes5 downloads5 reach2 impact
1000000 instances - 4 features - 2 classes - 0 missing values
No data.
306 runs0 likes4 downloads4 reach2 impact
1000000 instances - 4 features - 2 classes - 0 missing values
No data.
305 runs0 likes3 downloads3 reach2 impact
1000000 instances - 4 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…
2 runs0 likes0 downloads0 reach5 impact
100 instances - 4 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…
2 runs0 likes0 downloads0 reach5 impact
132 instances - 4 features - 0 classes - 0 missing values
Human Development Index [DATA] United Nations Development Program compiled an Index of Human Development. Column 1: Country(character) 2: Index 3: GNP GNP PER CAPITA RANK RANK - RANK HDI 1987 GNP RANK…
2 runs0 likes0 downloads0 reach5 impact
130 instances - 4 features - 0 classes - 0 missing values
One of the data sets used in the book "Analyzing Categorical Data" by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. Further details concerning the book, including information on statistical…
2 runs0 likes0 downloads0 reach5 impact
100 instances - 4 features - 0 classes - 0 missing values
DATA FILE: Data on patient deaths within 30 days of surgery in 131 U.S. hospitals. See Christiansen and Morris, Bayesian Biostatistics, D. Berry and D. Stangl, editors, 1996, Marcel Dekker, Inc. Data…
0 runs0 likes0 downloads0 reach5 impact
131 instances - 4 features - 0 classes - 0 missing values
File README ----------- smoothmeth A collection of the data sets used in the book "Smoothing Methods in Statistics," by Jeffrey S. Simonoff, Springer-Verlag, New York, 1996. Submitted by Jeff Simonoff…
0 runs0 likes0 downloads0 reach5 impact
2178 instances - 4 features - 0 classes - 0 missing values
Data on fluctuating proportions of marked cells in marrow from heterozygous Safari cats from a study of early hematopoiesis. The data included below are 11 time series of proportions of marked…
2 runs0 likes2 downloads2 reach5 impact
140 instances - 4 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…
2 runs0 likes1 downloads1 reach5 impact
468 instances - 4 features - 0 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 likes2 downloads2 reach5 impact
44 instances - 4 features - 0 classes - 0 missing values
Data Used in "A BAYESIAN APPROACH TO DATA DISCLOSURE: OPTIMAL INTRUDER BEHAVIOR FOR CONTINUOUS DATA" by Stephen E. Fienberg, Udi E. Makov, and Ashish P. Sanil Background: ========== In this paper we…
0 runs0 likes0 downloads0 reach5 impact
662 instances - 4 features - 0 classes - 0 missing values