{
  "id": 537972,
  "title": "mapping question",
  "url": "/competitions/child-mind-institute-problematic-internet-use/discussion/537972",
  "author_name": "thomas kern",
  "post_date": "2024-10-06T08:51:02.030000",
  "votes": 1,
  "comment_count": 0,
  "views": 0,
  "content": "<p>mapping_train<br>\nOut[3]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Spring': 3, 'Missing': 4}</p>\n<p>mapping_test<br>\nOut[4]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Missing': 3, 'Spring': 4}</p>\n<p>The above gives score of 0.471</p>\n<p>If concat train and test as in comments from<br>\n<a href=\"https://www.kaggle.com/code/abdmental01/cmi-best-single-model\" target=\"_blank\">https://www.kaggle.com/code/abdmental01/cmi-best-single-model</a><br>\ngives score of 0.459</p>\n<p><code>for col in cat_c:\n    all_values = pd.concat([train[col], test[col]]).unique()\n    mapping = {value: idx for idx, value in enumerate(all_values)}\n    train[col] = train[col].replace(mapping).astype(int)\n    test[col] = test[col].replace(mapping).astype(int)</code></p>",
  "messages": [
    {
      "id": 3008123,
      "postDate": "2024-10-06T08:51:02.030Z",
      "content": "<p>mapping_train<br>\nOut[3]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Spring': 3, 'Missing': 4}</p>\n<p>mapping_test<br>\nOut[4]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Missing': 3, 'Spring': 4}</p>\n<p>The above gives score of 0.471</p>\n<p>If concat train and test as in comments from<br>\n<a href=\"https://www.kaggle.com/code/abdmental01/cmi-best-single-model\" target=\"_blank\">https://www.kaggle.com/code/abdmental01/cmi-best-single-model</a><br>\ngives score of 0.459</p>\n<p><code>for col in cat_c:\n    all_values = pd.concat([train[col], test[col]]).unique()\n    mapping = {value: idx for idx, value in enumerate(all_values)}\n    train[col] = train[col].replace(mapping).astype(int)\n    test[col] = test[col].replace(mapping).astype(int)</code></p>",
      "rawMarkdown": "mapping_train\nOut[3]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Spring': 3, 'Missing': 4}\n\nmapping_test\nOut[4]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Missing': 3, 'Spring': 4}\n\nThe above gives score of 0.471\n\nIf concat train and test as in comments from\nhttps://www.kaggle.com/code/abdmental01/cmi-best-single-model\ngives score of 0.459\n\n`for col in cat_c:\n    all_values = pd.concat([train[col], test[col]]).unique()\n    mapping = {value: idx for idx, value in enumerate(all_values)}\n    train[col] = train[col].replace(mapping).astype(int)\n    test[col] = test[col].replace(mapping).astype(int)`",
      "votes": 1
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3008123": "mapping_train\nOut[3]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Spring': 3, 'Missing': 4}\n\nmapping_test\nOut[4]: {'Fall': 0, 'Summer': 1, 'Winter': 2, 'Missing': 3, 'Spring': 4}\n\nThe above gives score of 0.471\n\nIf concat train and test as in comments from\nhttps://www.kaggle.com/code/abdmental01/cmi-best-single-model\ngives score of 0.459\n\n`for col in cat_c:\n    all_values = pd.concat([train[col], test[col]]).unique()\n    mapping = {value: idx for idx, value in enumerate(all_values)}\n    train[col] = train[col].replace(mapping).astype(int)\n    test[col] = test[col].replace(mapping).astype(int)`"
  }
}