{
  "fetched_at": "2026-10-19T19:18:46Z",
  "source_url": "https://www.kaggle.com/competitions/aml-week-4/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Data for this competition is anonymised, however the feature names make clear what type of feature it is.  hccvs have not been named differently but they should be obvious to you.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "You goal is to use h2o to create a logistic regression model.  The target variable is called \"target\".  Features are anonymised but you will see that there are numeric, categorical and hccv features.  You are expected to deal sensibly with missings in train or test data, with numeric, categorical and hccv features.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "The evaluation metric is AUC.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "You are subject to all University rules.  Do not share this competition outside of the AML class.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}