{
  "fetched_at": "2026-10-20T03:57:00Z",
  "source_url": "https://www.kaggle.com/competitions/ee370000-introduction-to-machine-learning-lab2-new/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Files\nlab2_train.csv - the training set\nlab2_test.csv - the test set\nsubmission_sample.csv - a sample submission file in the correct format",
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    {
      "name": "Description",
      "content": "Dataset\n空氣品質監測資料\nSupervised learning: Binary air quality classification (Neg:0, Pos:1)\nLabel: Train: 1920 for Neg, 523 for Pos\n\nPreparing Dataset\nFor Kaggle challenge: You should use the training set and testing set provided by us. Regardless of the methods you choose during training, please ensure that the testing set is not used during the training phase. During testing, use the testing set to predict the corresponding labels and format the results for submission in Kaggle's required format to obtain competition scores.",
      "isPublished": false,
      "hasIsPublished": false,
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    },
    {
      "name": "Evaluation",
      "content": "Submissions are evaluated on [recall] between the predicted probability and the observed target.\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the target variable. The file should contain a header and have the following format:\n\n    ID,label\n    2,0\n    5,0\n    6,0\n    etc.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "1. The competition will end at the end of class (11/29 13:00)\n2. Late submission is not allowed\n3. Try any advanced method to improve performance\n4. This Kaggle challenge is provided as a bonus for everyone (bonus: 1% of total grade for the top 25% on private score)\n5. The lab will release on 11/26 (Lab demo: 100% points of every lab)",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "abstract",
      "content": "This is a binary air quality classification.\nDifferent from Lab demo, you can try any advanced method you know to improve performance.",
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      "hasIsPublished": false,
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    }
  ]
}