Data

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

class (target) | nominal | 5 unique values 0 missing | |

parents | nominal | 3 unique values 0 missing | |

has_nurs | nominal | 5 unique values 0 missing | |

form | nominal | 4 unique values 0 missing | |

children | nominal | 4 unique values 0 missing | |

housing | nominal | 3 unique values 0 missing | |

finance | nominal | 2 unique values 0 missing | |

social | nominal | 3 unique values 0 missing | |

health | nominal | 3 unique values 0 missing |

0

Minimal mutual information between the nominal attributes and the target attribute.

0.02

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.5

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

2

The minimal number of distinct values among attributes of the nominal type.

0

Second quartile (Median) of skewness among attributes of the numeric type.

0.92

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.96

Maximum mutual information between the nominal attributes and the target attribute.

0

Second quartile (Median) of standard deviation of attributes of the numeric type.

0.96

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

5

The maximum number of distinct values among attributes of the nominal type.

0.07

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

10.63

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.89

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.04

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.96

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.98

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

0.17

Third quartile of mutual information between the nominal attributes and the target attribute.

0.93

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.07

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.93

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.89

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

0

Third quartile of standard deviation of attributes of the numeric type.

0.04

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.96

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.16

Average mutual information between the nominal attributes and the target attribute.

9.58

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.93

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.07

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.93

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

3.38

Average number of distinct values among the attributes of the nominal type.

0.01

First quartile of mutual information between the nominal attributes and the target attribute.

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.89

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

0.92

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

0.04

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0.92

Standard deviation of the number of distinct values among attributes of the nominal type.

0

First quartile of standard deviation of attributes of the numeric type.

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.93

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

0

Second quartile (Median) of kurtosis among attributes of the numeric type.

0.92

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

0.83

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

0

Second quartile (Median) of means among attributes of the numeric type.

0.99

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

0.34

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

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