{ "data_id": "196", "name": "autoMpg", "exact_name": "autoMpg", "version": 1, "version_label": "1", "description": "**Author**: \n**Source**: Unknown - \n**Please cite**: \n\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n Identifier attribute deleted.\n\n As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction\n using instance-based learning with encoding length selection. In Progress\n in Connectionist-Based Information Systems. Singapore: Springer-Verlag.\n\n !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n\n 1. Title: Auto-Mpg Data\n \n 2. Sources:\n (a) Origin: This dataset was taken from the StatLib library which is\n maintained at Carnegie Mellon University. The dataset was \n used in the 1983 American Statistical Association Exposition.\n (c) Date: July 7, 1993\n \n 3. Past Usage:\n - See 2b (above)\n - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.\n In Proceedings on the Tenth International Conference of Machine \n Learning, 236-243, University of Massachusetts, Amherst. Morgan\n Kaufmann.\n \n 4. Relevant Information:\n \n This dataset is a slightly modified version of the dataset provided in\n the StatLib library. In line with the use by Ross Quinlan (1993) in\n predicting the attribute \"mpg\", 8 of the original instances were removed \n because they had unknown values for the \"mpg\" attribute. The original \n dataset is available in the file \"auto-mpg.data-original\".\n \n \"The data concerns city-cycle fuel consumption in miles per gallon,\n to be predicted in terms of 3 multivalued discrete and 5 continuous\n attributes.\" (Quinlan, 1993)\n \n 5. Number of Instances: 398\n \n 6. Number of Attributes: 9 including the class attribute\n \n 7. Attribute Information:\n \n 1. mpg: continuous\n 2. cylinders: multi-valued discrete\n 3. displacement: continuous\n 4. horsepower: continuous\n 5. weight: continuous\n 6. acceleration: continuous\n 7. model year: multi-valued discrete\n 8. origin: multi-valued discrete\n 9. car name: string (unique for each instance)\n \n 8. Missing Attribute Values: horsepower has 6 missing values", "format": "ARFF", "uploader": "Jan van Rijn", "uploader_id": 1, "visibility": "public", "creator": null, "contributor": "StatLib", "date": "2014-04-23 13:16:22", "update_comment": null, "last_update": "2014-04-23 13:16:22", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/3633\/dataset_2182_autoMpg.arff", "kaggle_url": null, "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 2, "suggest": { "input": [ "autoMpg", "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Identifier attribute deleted. As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1. Title: Auto-Mpg Data 2. Sources: (a) Origin: This dataset was taken from the StatLib library which " ], "weight": 5 }, "qualities": { "NumberOfInstances": 398, "NumberOfFeatures": 8, "NumberOfClasses": 0, "NumberOfMissingValues": 6, "NumberOfInstancesWithMissingValues": 6, "NumberOfNumericFeatures": 5, "NumberOfSymbolicFeatures": 3, "MaxStdDevOfNumericAtts": 846.841774197327, "MinorityClassPercentage": null, "PercentageOfNumericFeatures": 62.5, "Quartile3MeansOfNumericAtts": 1581.9252512562816, "CfsSubsetEval_DecisionStumpAUC": null, "RandomTreeDepth2AUC": null, "J48.00001.ErrRate": null, "MeanAttributeEntropy": null, "MinorityClassSize": null, "PercentageOfSymbolicFeatures": 37.5, "Quartile3MutualInformation": null, "CfsSubsetEval_DecisionStumpErrRate": null, "RandomTreeDepth2ErrRate": null, "J48.00001.Kappa": null, "MeanKurtosisOfNumericAtts": -0.18529258343933788, "NaiveBayesAUC": null, "Quartile1AttributeEntropy": null, "Quartile3SkewnessOfNumericAtts": 0.9034857233527331, 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