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hayes-roth_clean

hayes-roth_clean

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Author: Barbara and Frederick Hayes-Roth Source: [original](https://archive.ics.uci.edu/ml/datasets/Hayes-Roth) - Please cite: 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 Information: (a) Creators: Barbara and Frederick Hayes-Roth (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 (c) Date: March, 1989 Attribute Information: -- 1. name: distinct for each instance and represented numerically -- 2. hobby: nominal values ranging between 1 and 3 -- 3. age: nominal values ranging between 1 and 4 -- 4. educational level: nominal values ranging between 1 and 4 -- 5. marital status: nominal values ranging between 1 and 4 -- 6. class: nominal value between 1 and 3 Detailed description of the experiment: 1. 3 categories (1, 2, and neither -- which I call 3) -- some of the instances could be classified in either class 1 or 2, and they have been evenly distributed between the two classes 2. 5 Attributes -- A. name (a randomly-generated number between 1 and 132) -- B. hobby (a randomly-generated number between 1 and 3) -- C. age (a number between 1 and 4) -- D. education level (a number between 1 and 4) -- E. marital status (a number between 1 and 4) 3. Classification: -- only attributes C-E are diagnostic; values for A and B are ignored -- Class Neither: if a 4 occurs for any attribute C-E -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E 4. Prototypes: -- Class 1: 111 -- Class 2: 222 -- Class Either: 333 -- Class Neither: 444 ----- We have redefined the number of classes to account for the real number of observations.

5 features

class (target)nominal3 unique values
0 missing
hobbynumeric3 unique values
0 missing
agenumeric4 unique values
0 missing
educational_levelnumeric4 unique values
0 missing
marital_statusnumeric4 unique values
0 missing

19 properties

160
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.41
Average class difference between consecutive instances.
80
Percentage of numeric attributes.
0.03
Number of attributes divided by the number of instances.
20
Percentage of nominal attributes.
40.63
Percentage of instances belonging to the most frequent class.
65
Number of instances belonging to the most frequent class.
19.38
Percentage of instances belonging to the least frequent class.
31
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
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