Tic-Tac-Toe Endgame

Donated on 8/18/1991

Binary classification task on possible configurations of tic-tac-toe game

Dataset Characteristics

Multivariate

Subject Area

Games

Associated Tasks

Classification

Feature Type

Categorical

# Instances

958

# Features

9

Dataset Information

Additional Information

This database encodes the complete set of possible board configurations at the end of tic-tac-toe games, where "x" is assumed to have played first. The target concept is "win for x" (i.e., true when "x" has one of 8 possible ways to create a "three-in-a-row"). Interestingly, this raw database gives a stripped-down decision tree algorithm (e.g., ID3) fits. However, the rule-based CN2 algorithm, the simple IB1 instance-based learning algorithm, and the CITRE feature-constructing decision tree algorithm perform well on it.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
classTargetCategoricalno
top-left-squareFeatureCategoricalno
top-middle-squareFeatureCategoricalno
top-right-squareFeatureCategoricalno
middle-left-squareFeatureCategoricalno
middle-middle-squareFeatureCategoricalno
middle-right-squareFeatureCategoricalno
bottom-left-squareFeatureCategoricalno
bottom-middle-squareFeatureCategoricalno
bottom-right-squareFeatureCategoricalno

0 to 10 of 10

Additional Variable Information

1. top-left-square: {x,o,b} 2. top-middle-square: {x,o,b} 3. top-right-square: {x,o,b} 4. middle-left-square: {x,o,b} 5. middle-middle-square: {x,o,b} 6. middle-right-square: {x,o,b} 7. bottom-left-square: {x,o,b} 8. bottom-middle-square: {x,o,b} 9. bottom-right-square: {x,o,b} 10. Class: {positive,negative}

Baseline Model Performance

Papers Citing this Dataset

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

By Benjamin Letham, Cynthia Rudin, Tyler McCormick, David Madigan. 2015

Published in Annals of Applied Statistics 2015, Vol. 9, No. 3, 1350-1371.

Fuzzy based binary feature profiling for modus operandi analysis

By Mahawaga Chamikara, Akalanka Galappaththi, Roshan Yapa, Ruwan Nawarathna, Saluka Kodituwakku, Jagath Gunatilake, Aththanapola Jayathilake, Liwan Liyanage. 2015

Published in PeerJ PrePrints.

Feature selection with test cost constraint

By Fan Min, Qinghua Hu, William Zhu. 2012

Published in ArXiv.

Sequences Classification by Least General Generalisations

By Frédéric Tantini, Alain Terlutte, Fabien Torre. 2010

Published in ICGI.

0 to 4 of 4

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4 citations
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Creators

David Aha

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