{
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    "name": "visualizing_soil",
    "exact_name": "visualizing_soil",
    "version": 4,
    "version_label": null,
    "description": "Dataset used in the tabular data benchmark https:\/\/github.com\/LeoGrin\/tabular-benchmark,  \n                                  transformed in the same way. This dataset belongs to the \"regression on categorical and\n                                  numerical features\" benchmark. Original description: \n \n**Author**:   \n**Source**: Unknown - Date unknown  \n**Please cite**:   \n\nThis S dump contains 22 data sets from the\nbook Visualizing Data published by\nHobart Press (books@hobart.com).\nThe dump was created by data.dump()\nand can be read back into S by data.restore().\nThe name of each S data set is the name of\nthe data set used in the book. To find the\ndescription of the data set in the book look\nunder the entry - data, name - in the index.\nFor example, one data set is barley.\nTo find the description of barley, look\nin the index under the entry - data, barley.\n\nFile: ..\/data\/visualizing\/soil.csv\n\n\nInformation about the dataset\nCLASSTYPE: numeric\nCLASSINDEX: none specific",
    "format": "arff",
    "uploader": "Leo Grin",
    "uploader_id": 26324,
    "visibility": "public",
    "creator": "\"Hobart Press\"",
    "contributor": "\"Leo Grin\"",
    "date": "2022-06-21 10:29:54",
    "update_comment": null,
    "last_update": "2022-06-21 10:29:54",
    "licence": "Public",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/old.openml.org\/data\/download\/22103152\/dataset",
    "default_target_attribute": "track",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "visualizing_soil",
            "Dataset used in the tabular data benchmark https:\/\/github.com\/LeoGrin\/tabular-benchmark, transformed in the same way. This dataset belongs to the \"regression on categorical and numerical features\" benchmark. Original description: This S dump contains 22 data sets from the book Visualizing Data published by Hobart Press (books@hobart.com). The dump was created by data.dump() and can be read back into S by data.restore(). The name of each S data set is the name of the data set used in the book. To "
        ],
        "weight": 5
    },
    "qualities": {
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        "NumberOfFeatures": 5,
        "NumberOfClasses": 0,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 4,
        "NumberOfSymbolicFeatures": 1,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 1,
        "PercentageOfBinaryFeatures": 20,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": 0.9859953703703703,
        "PercentageOfMissingValues": 0,
        "Dimensionality": 0.0005786367318597385,
        "PercentageOfNumericFeatures": 80,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 20,
        "MajorityClassSize": null
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        }
    ],
    "features": [
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