{
  "fetched_at": "2026-10-24T23:40:01Z",
  "source_url": "https://www.kaggle.com/competitions/adult-readmission/data",
  "pages": [
    {
      "name": "rules",
      "content": "Do your best to boost model performance and have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "data-description",
      "content": "X_train.csv is the files with all the features and target label.\n\nCODE_REHOSP is the target, where 0=Non-readmitted, 1=Readmitted\n\nPlease follow the format in sample_submission.csv and submit your prediction.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "Description",
      "content": "The dataset (X_train) contains all the Tsfresh features and original varialbes.\n\nThe aim is to predict the target readmission (CODE_REHOSP, where 1=readmitted, 0=non-readmitted)\n\nFinal submission file should be like the sample_submission.csv file with id column and your predicted probability.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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    },
    {
      "name": "Evaluation",
      "content": "Evaluation is based on roc_auc. \n\nThe system will use the roc_auc_score from sklearn metrics.\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html\n\nThere is no external testing set. So your score will be based on the original training set. Be aware to set aside your validation set from it.",
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      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}