{
  "id": 23337,
  "title": "Some features missing?(Date entry[Dn]present, feature[F(n-1)] missing)",
  "url": "/competitions/bosch-production-line-performance/discussion/23337",
  "author_name": "",
  "post_date": "2016-09-04T04:20:02.640Z",
  "votes": 1,
  "comment_count": 1,
  "views": 354,
  "content": "<p>Hello, \nThe train_date table has entries for time values, such as L0_S7_D140, which would mean feature L0_S7_F139 was read. However, L0_S7_F139 does not exist in the feature tables(neither train_numeric or train_categorical). This happens for 21 features. (Date entry exists, feature missing). </p>\n\n<p>Has anyone else encountered this error? If so, please share the explanation for this anomaly. </p>\n\n<p>Thanks and apologies for any oversights, \nKumar</p>",
  "messages": [
    {
      "id": "133859",
      "postDate": "09/04/2016 04:20:02",
      "content": "<p>Hello, \nThe train_date table has entries for time values, such as L0_S7_D140, which would mean feature L0_S7_F139 was read. However, L0_S7_F139 does not exist in the feature tables(neither train_numeric or train_categorical). This happens for 21 features. (Date entry exists, feature missing). </p>\n\n<p>Has anyone else encountered this error? If so, please share the explanation for this anomaly. </p>\n\n<p>Thanks and apologies for any oversights, \nKumar</p>",
      "rawMarkdown": "Hello, \r\nThe train_date table has entries for time values, such as L0_S7_D140, which would mean feature L0_S7_F139 was read. However, L0_S7_F139 does not exist in the feature tables(neither train_numeric or train_categorical). This happens for 21 features. (Date entry exists, feature missing). \r\n\r\nHas anyone else encountered this error? If so, please share the explanation for this anomaly. \r\n\r\nThanks and apologies for any oversights, \r\nKumar",
      "votes": null
    },
    {
      "id": "133862",
      "postDate": "09/04/2016 05:59:51",
      "content": "<p>I do not treat date feature *DN+1 as a feature describing the time of measuring the feature *FN. I treat it as the time of crossing the check point number N. In the case of long conveyor there can be few time check points with no numeric or categorical measurements between. In the case of compact station, there may be no time measurement between few numeric or categorical measurements.</p>",
      "rawMarkdown": "I do not treat date feature *DN+1 as a feature describing the time of measuring the feature *FN. I treat it as the time of crossing the check point number N. In the case of long conveyor there can be few time check points with no numeric or categorical measurements between. In the case of compact station, there may be no time measurement between few numeric or categorical measurements.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 133862,
      "author_name": "sionek",
      "author_url": "",
      "post_date": "09/04/2016 05:59:51",
      "content": "<p>I do not treat date feature *DN+1 as a feature describing the time of measuring the feature *FN. I treat it as the time of crossing the check point number N. In the case of long conveyor there can be few time check points with no numeric or categorical measurements between. In the case of compact station, there may be no time measurement between few numeric or categorical measurements.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "133859": "Hello, \r\nThe train_date table has entries for time values, such as L0_S7_D140, which would mean feature L0_S7_F139 was read. However, L0_S7_F139 does not exist in the feature tables(neither train_numeric or train_categorical). This happens for 21 features. (Date entry exists, feature missing). \r\n\r\nHas anyone else encountered this error? If so, please share the explanation for this anomaly. \r\n\r\nThanks and apologies for any oversights, \r\nKumar",
    "133862": "I do not treat date feature *DN+1 as a feature describing the time of measuring the feature *FN. I treat it as the time of crossing the check point number N. In the case of long conveyor there can be few time check points with no numeric or categorical measurements between. In the case of compact station, there may be no time measurement between few numeric or categorical measurements."
  },
  "source": "meta"
}