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    "description": "**Author**: [Isabelle Guyon](isabelle@clopinet.com)  \r\n**Source**: [Agnostic Learning vs. Prior Knowledge Challenge](http:\/\/www.agnostic.inf.ethz.ch)  \r\n**Please cite**: None\r\n\r\n\r\nDataset from the Agnostic Learning vs. Prior Knowledge Challenge (http:\/\/www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLVA, GINA, NOVA, HIVA, ADA). The purpose of the challenge was to check if the performance of domain-specific feature engineering (prior knowledge) can be met by algorithms that were trained on data without any domain-specific knowledge (agnostic). For the latter, the data was anonymised and preprocessed in a way that makes them uninterpretable. \r\n\r\nThis dataset contains the agnostic (smashed) version of a data set from the US census bureau for the time span June 2005 - September 2006. Similar data set on OpenML is called __adult__. The raw data from the census bureau is also known as the Adult database in the UCI machine-learning repository. \r\n\r\n### Topic\r\n\r\nThe task of ADA is to discover high revenue people from census data. This is a two-class\r\nclassification problem. The raw data from the census bureau is known as the Adult\r\ndatabase in the UCI machine-learning repository. It contains continuous, binary and\r\ncategorical variables. The \u201cprior knowledge track\u201d has access to the original features and\r\ntheir identity. The agnostic track has access to a preprocessed numeric representation\r\neliminating categorical variables. \r\n\r\n### Source\r\n\r\nOriginal owners\r\nThis data was extracted from the census bureau database found at\r\nhttp:\/\/www.census.gov\/ftp\/pub\/DES\/www\/welcome.html\r\nDonor: Ronny Kohavi and Barry Becker,\r\n Data Mining and Visualization\r\n Silicon Graphics.\r\n e-mail: ronnyk@sgi.com for questions\r\n\r\nDataset from: http:\/\/www.agnostic.inf.ethz.ch\/datasets.php\r\n\r\n### Preprocessing\r\n\r\nIn [this documentation](http:\/\/clopinet.com\/isabelle\/Projects\/agnostic\/Dataset.pdf) the organisers of the challenge describe the steps they performed to come up with the __agnostic__ data. The 14 original attributes (features) include age, workclass,  education,\r\nmarital status, occupation, native country, etc. It contains continuous, binary and categorical features. This dataset is from the \"agnostic learning track\", i.e. has access to a preprocessed numeric representation eliminating categorical variables, but the identity of the features is not revealed.\r\n\r\n### Additional Info\r\n\r\nThis dataset contains samples from both training and validation datasets. Modified by TunedIT (converted to ARFF format).\r\n\r\nData type: non-sparse\r\nNumber of features: 48\r\nNumber of examples and check-sums:\r\nPos_ex Neg_ex Tot_ex Check_sum\r\nTrain  1029  3118  4147 6798109.00\r\nValid   103   312   415 681151.00\r\n\r\n\r\n",
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}