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nursery

nursery

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Nursery Database 2. Sources: (a) Creator: Vladislav Rajkovic et al. (13 experts) (b) Donors: Marko Bohanec (marko.bohanec@ijs.si) Blaz Zupan (blaz.zupan@ijs.si) (c) Date: June, 1997 3. Past Usage: The hierarchical decision model, from which this dataset is derived, was first presented in M. Olave, V. Rajkovic, M. Bohanec: An application for admission in public school systems. In (I. Th. M. Snellen and W. B. H. J. van de Donk and J.-P. Baquiast, editors) Expert Systems in Public Administration, pages 145-160. Elsevier Science Publishers (North Holland)}, 1989. Within machine-learning, this dataset was used for the evaluation of HINT (Hierarchy INduction Tool), which was proved to be able to completely reconstruct the original hierarchical model. This, together with a comparison with C4.5, is presented in B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear) 4. Relevant Information Paragraph: Nursery Database was derived from a hierarchical decision model originally developed to rank applications for nursery schools. It was used during several years in 1980's when there was excessive enrollment to these schools in Ljubljana, Slovenia, and the rejected applications frequently needed an objective explanation. The final decision depended on three subproblems: occupation of parents and child's nursery, family structure and financial standing, and social and health picture of the family. The model was developed within expert system shell for decision making DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The hierarchical model ranks nursery-school applications according to the following concept structure: NURSERY Evaluation of applications for nursery schools . EMPLOY Employment of parents and child's nursery . . parents Parents' occupation . . has_nurs Child's nursery . STRUCT_FINAN Family structure and financial standings . . STRUCTURE Family structure . . . form Form of the family . . . children Number of children . . housing Housing conditions . . finance Financial standing of the family . SOC_HEALTH Social and health picture of the family . . social Social conditions . . health Health conditions Input attributes are printed in lowercase. Besides the target concept (NURSERY) the model includes four intermediate concepts: EMPLOY, STRUCT_FINAN, STRUCTURE, SOC_HEALTH. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see http://www-ai.ijs.si/BlazZupan/nursery.html). The Nursery Database contains examples with the structural information removed, i.e., directly relates NURSERY to the eight input attributes: parents, has_nurs, form, children, housing, finance, social, health. Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods. 5. Number of Instances: 12960 (instances completely cover the attribute space) 6. Number of Attributes: 8 7. Attribute Values: parents usual, pretentious, great_pret has_nurs proper, less_proper, improper, critical, very_crit form complete, completed, incomplete, foster children 1, 2, 3, more housing convenient, less_conv, critical finance convenient, inconv social non-prob, slightly_prob, problematic health recommended, priority, not_recom 8. Missing Attribute Values: none 9. Class Distribution (number of instances per class) class N N[%] ------------------------------ not_recom 4320 (33.333 %) recommend 2 ( 0.015 %) very_recom 328 ( 2.531 %) priority 4266 (32.917 %) spec_prior 4044 (31.204 %) Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

9 features

class (target)nominal5 unique values
0 missing
parentsnominal3 unique values
0 missing
has_nursnominal5 unique values
0 missing
formnominal4 unique values
0 missing
childrennominal4 unique values
0 missing
housingnominal3 unique values
0 missing
financenominal2 unique values
0 missing
socialnominal3 unique values
0 missing
healthnominal3 unique values
0 missing

62 properties

12960
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
5
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.
0
Number of numeric attributes.
9
Number of nominal attributes.
100
Percentage of nominal attributes.
0.17
Third quartile of mutual information between the nominal attributes and the target attribute.
5
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
1.58
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
0.02
Percentage of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
1.01
Standard deviation of the number of distinct values among attributes of the nominal type.
1.71
Average entropy of the attributes.
2
Number of instances belonging to the least frequent class.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of skewness among attributes of the numeric type.
Mean of means among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.24
Average class difference between consecutive instances.
0.16
Average mutual information between the nominal attributes and the target attribute.
1.58
Second quartile (Median) of entropy among attributes.
1.72
Entropy of the target attribute values.
9.58
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
3.56
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
10.63
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
33.33
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
4320
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.32
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
11.11
Percentage of binary attributes.
2
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.96
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.

17 tasks

1271 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
199 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
179 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
170 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
169 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
166 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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