Nursery
Donated on 5/31/1997
Nursery Database was derived from a hierarchical decision model originally developed to rank applications for nursery schools.
Dataset Characteristics
Multivariate
Subject Area
Social Science
Associated Tasks
Classification
Feature Type
Categorical
# Instances
12960
# Features
8
Dataset Information
Additional Information
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.
Has Missing Values?
No
Introductory Paper
By M. Olave, V. Rajkovic, M. Bohanec. 1989
Published in Expert Systems in Public Administration
Variables Table
Variable Name | Role | Type | Description | Units | Missing Values |
---|---|---|---|---|---|
parents | Feature | Categorical | usual, pretentious, great_pret | no | |
has_nurs | Feature | Categorical | proper, less_proper, improper, critical, very_crit | no | |
form | Feature | Categorical | complete, completed, incomplete, foster | no | |
children | Feature | Categorical | 1, 2, 3, more | no | |
housing | Feature | Categorical | convenient, less_conv, critical | no | |
finance | Feature | Categorical | convenient, inconv | no | |
social | Feature | Categorical | non-prob, slightly_prob, problematic | no | |
health | Feature | Categorical | recommended, priority, not_recom | no | |
class | Target | Categorical | recommended, priority, not_recom | no |
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Additional Variable Information
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
Baseline Model Performance
Dataset Files
File | Size |
---|---|
nursery.data | 1 MB |
nursery.names | 4 KB |
nursery.c45-names | 501 Bytes |
Papers Citing this Dataset
Sort by Year, desc
By Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou. 2018
Published in ArXiv.
By David Wu, Tony Feng, Michael Naehrig, Kristin Lauter. 2016
Published in PoPETs.
By Shen-Shyang Ho, Harry Wechsler. 2012
Published in ArXiv.
0 to 5 of 10
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset nursery = fetch_ucirepo(id=76) # data (as pandas dataframes) X = nursery.data.features y = nursery.data.targets # metadata print(nursery.metadata) # variable information print(nursery.variables)
Rajkovic, V. (1989). Nursery [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5P88W.
Keywords
Creators
Vladislav Rajkovic
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.