{ "data_id": "25", "name": "tic-tac-toe", "exact_name": "tic-tac-toe", "version": 1, "version_label": "1", "description": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\n1. Title: Tic-Tac-Toe Endgame database\n \n 2. Source Information\n -- Creator: David W. Aha (aha@cs.jhu.edu)\n -- Donor: David W. Aha (aha@cs.jhu.edu)\n -- Date: 19 August 1991\n \n 3. Known Past Usage: \n 1. Matheus,~C.~J., & Rendell,~L.~A. (1989). Constructive\n induction on decision trees. In {it Proceedings of the\n Eleventh International Joint Conference on Artificial Intelligence} \n (pp. 645--650). Detroit, MI: Morgan Kaufmann.\n -- CITRE was applied to 100-instance training and 200-instance test\n sets. In a study using various amounts of domain-specific\n knowledge, its highest average accuracy was 76.7% (using the\n final decision tree created for testing).\n \n 2. Matheus,~C.~J. (1990). Adding domain knowledge to SBL through\n feature construction. In {it Proceedings of the Eighth National\n Conference on Artificial Intelligence} (pp. 803--808). \n Boston, MA: AAAI Press.\n -- Similar experiments with CITRE, includes learning curves up\n to 500-instance training sets but used _all_ instances in the\n database for testing. Accuracies reached above 90%, but specific\n values are not given (see Chris's dissertation for more details).\n \n 3. Aha,~D.~W. (1991). Incremental constructive induction: An instance-based\n approach. In {it Proceedings of the Eighth International Workshop\n on Machine Learning} (pp. 117--121). Evanston, ILL: Morgan Kaufmann.\n -- Used 70% for training, 30% of the instances for testing, evaluated\n over 10 trials. Results reported for six algorithms:\n -- NewID: 84.0%\n -- CN2: 98.1% \n -- MBRtalk: 88.4%\n -- IB1: 98.1% \n -- IB3: 82.0%\n -- IB3-CI: 99.1%\n -- Results also reported when adding an additional 10 irrelevant \n ternary-valued attributes; similar _relative_ results except that\n IB1's performance degraded more quickly than the others.\n \n 4. Relevant Information:\n \n This database encodes the complete set of possible board configurations\n at the end of tic-tac-toe games, where \"x\" is assumed to have played\n first. The target concept is \"win for x\" (i.e., true when \"x\" has one\n of 8 possible ways to create a \"three-in-a-row\"). \n \n Interestingly, this raw database gives a stripped-down decision tree\n algorithm (e.g., ID3) fits. However, the rule-based CN2 algorithm, the\n simple IB1 instance-based learning algorithm, and the CITRE \n feature-constructing decision tree algorithm perform well on it.\n \n 5. Number of Instances: 958 (legal tic-tac-toe endgame boards)\n \n 6. Number of Attributes: 9, each corresponding to one tic-tac-toe square\n \n 7. Attribute Information: (x=player x has taken, o=player o has taken, b=blank)\n \n 1. top-left-square: {x,o,b}\n 2. top-middle-square: {x,o,b}\n 3. top-right-square: {x,o,b}\n 4. middle-left-square: {x,o,b}\n 5. middle-middle-square: {x,o,b}\n 6. middle-right-square: {x,o,b}\n 7. bottom-left-square: {x,o,b}\n 8. bottom-middle-square: {x,o,b}\n 9. bottom-right-square: {x,o,b}\n 10. Class: {positive,negative}\n \n 8. Missing Attribute Values: None\n \n 9. Class Distribution: About 65.3% are positive (i.e., wins for \"x\")\n\n Information about the dataset\n CLASSTYPE: nominal\n CLASSINDEX: last", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": null, "contributor": null, "date": "2014-04-06 23:22:59", "update_comment": null, "last_update": "2014-04-06 23:22:59", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/50\/dataset_50_tic-tac-toe.arff", "default_target_attribute": "Class", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "tic-tac-toe", "1. Title: Tic-Tac-Toe Endgame database 2. Source Information -- Creator: David W. Aha (aha@cs.jhu.edu) -- Donor: David W. Aha (aha@cs.jhu.edu) -- Date: 19 August 1991 3. Known Past Usage: 1. Matheus,~C.~J., & Rendell,~L.~A. (1989). Constructive induction on decision trees. In {it Proceedings of the Eleventh International Joint Conference on Artificial Intelligence} (pp. 645--650). Detroit, MI: Morgan Kaufmann. -- CITRE was applied to 100-instance training and 200-instance test sets. In a study u " ], "weight": 5 }, "qualities": { "NumberOfInstances": 958, "NumberOfFeatures": 10, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 0, "NumberOfSymbolicFeatures": 10, "REPTreeDepth1Kappa": 0.52961409269228632, "CfsSubsetEval_kNN1NAUC": 0.74351399207051849, "StdvNominalAttDistinctValues": 0.31622776601683794, "J48.001.ErrRate": 0.18580375782881003, "MeanNominalAttDistinctValues": 2.8999999999999999, "Quartile1SkewnessOfNumericAtts": null, "REPTreeDepth2AUC": 0.82542630586242738, "CfsSubsetEval_kNN1NErrRate": 0.23903966597077245, "kNN1NAUC": 0.99627343238769772, "J48.001.Kappa": 0.57719505301054275, "MeanSkewnessOfNumericAtts": null, "Quartile1StdDevOfNumericAtts": null, "REPTreeDepth2ErrRate": 0.20354906054279751, "CfsSubsetEval_kNN1NKappa": 0.41922178864715764, "kNN1NErrRate": 0.028183716075156576, "MajorityClassPercentage": 65.34446764091858, "MeanStdDevOfNumericAtts": null, 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