{
  "id": 540660,
  "title": "In test.parquet, what does the column is_scored mean?",
  "url": "/competitions/jane-street-real-time-market-data-forecasting/discussion/540660",
  "author_name": "",
  "post_date": "2024-10-15T15:10:22.836437300Z",
  "votes": 3,
  "comment_count": 1,
  "views": 0,
  "content": "<p>In test.parquet, what does the column is_scored mean?</p>",
  "messages": [
    {
      "id": "3018178",
      "postDate": "10/15/2024 15:10:22",
      "content": "<p>In test.parquet, what does the column is_scored mean?</p>",
      "rawMarkdown": "In test.parquet, what does the column is_scored mean?",
      "votes": null
    },
    {
      "id": "3018247",
      "postDate": "10/15/2024 16:21:48",
      "content": "<p>Certain dates are not subjected to scoring. These may include interim periods between the training and public leaderboard/ public leaderboard to private leaderboard. In such cases, is_scored == 0. You can choose to make a condition like the ones below-<br>\n<code>if is_scored == 0, then submit a constant as a prediction, else continue with the inference</code><br>\n<a href=\"https://www.kaggle.com/jiaoyouzhang\" target=\"_blank\">@jiaoyouzhang</a> </p>",
      "rawMarkdown": "Certain dates are not subjected to scoring. These may include interim periods between the training and public leaderboard/ public leaderboard to private leaderboard. In such cases, is_scored == 0. You can choose to make a condition like the ones below-\n`if is_scored == 0, then submit a constant as a prediction, else continue with the inference`\n@jiaoyouzhang",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3018247,
      "author_name": "ravi20076",
      "author_url": "",
      "post_date": "10/15/2024 16:21:48",
      "content": "<p>Certain dates are not subjected to scoring. These may include interim periods between the training and public leaderboard/ public leaderboard to private leaderboard. In such cases, is_scored == 0. You can choose to make a condition like the ones below-<br>\n<code>if is_scored == 0, then submit a constant as a prediction, else continue with the inference</code><br>\n<a href=\"https://www.kaggle.com/jiaoyouzhang\" target=\"_blank\">@jiaoyouzhang</a> </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3018178": "In test.parquet, what does the column is_scored mean?",
    "3018247": "Certain dates are not subjected to scoring. These may include interim periods between the training and public leaderboard/ public leaderboard to private leaderboard. In such cases, is_scored == 0. You can choose to make a condition like the ones below-\n`if is_scored == 0, then submit a constant as a prediction, else continue with the inference`\n@jiaoyouzhang"
  },
  "source": "meta"
}