{
  "id": 457238,
  "title": "How using hours can deal with device-unweared problem?",
  "url": "/competitions/child-mind-institute-detect-sleep-states/discussion/457238",
  "author_name": "Demyan Pavlyshenko",
  "post_date": "2023-11-23T16:17:43.855000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Note: In this post, I'll be talking about neural networks</p>\n<h1>Introduction</h1>\n<p>In this competition we are given with series and events data and our task is to detect whether a child is sleeping or not by <strong>break points</strong>, but there are also some samples where a child doesn't wear the device, and that's the main problem of the competition. And it's not easy to detect, since the device is supposed to measure angle of an arm by fluctuation, and when the device is unweared, it tries to capture every single fluctuation, and that confuses our model</p>\n<h1>How hours can help us to deal with those samples?</h1>\n<p>If we see at the hour distribution showed below, we can see that there is no onset and wakeup events at some hours:<br>\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8913839%2F93ec145f17edce432e7f526ea66c411d%2Fdownload.png?generation=1700753722980321&amp;alt=media\" alt=\"hour distribution\"><br>\nSo, for example, if there is a pattern, which seems to show an onset event, and it's at 19 o'clock (if we look at the hour distribution above, we'll see there's no onset events at that hour), the model will classifiy that point neither an onset event, nor a wakeup event.</p>\n<h1>The risk of overfitting</h1>\n<p>But we also have to be cautious of using hours, because since the test set is hidden, there's also can be some surprises, like samples which are really onset events, but because it's at 19 o'clock (for example), it'll be misclassified, in other words, it's <strong>overfitting</strong>. And there's many ways to avoid it. For example, we can use hours only in some epochs, or we can use hours only by some probability each time, and the probability can be scheduled over epochs and so on. <br>\n<strong>Note</strong>: <em>Not using hours means that we disable hours, for example, if we are using embeddings, we set all embedding values to zero, or set hours as NaN values, or add <code>-1</code> hour as a disabled hour and so on</em></p>\n<p>But if we are cautious of this sort of overfitting of using hours, we have to evaluate the model without using hours, and deploy it without using hours too.</p>\n<p>What do you think about it? Hope it was helpful!</p>\n<p>Good luck in the competition!</p>",
  "messages": [
    {
      "id": 2535803,
      "postDate": "2023-11-23T16:17:43.857Z",
      "content": "<p>Note: In this post, I'll be talking about neural networks</p>\n<h1>Introduction</h1>\n<p>In this competition we are given with series and events data and our task is to detect whether a child is sleeping or not by <strong>break points</strong>, but there are also some samples where a child doesn't wear the device, and that's the main problem of the competition. And it's not easy to detect, since the device is supposed to measure angle of an arm by fluctuation, and when the device is unweared, it tries to capture every single fluctuation, and that confuses our model</p>\n<h1>How hours can help us to deal with those samples?</h1>\n<p>If we see at the hour distribution showed below, we can see that there is no onset and wakeup events at some hours:<br>\n<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8913839%2F93ec145f17edce432e7f526ea66c411d%2Fdownload.png?generation=1700753722980321&amp;alt=media\" alt=\"hour distribution\"><br>\nSo, for example, if there is a pattern, which seems to show an onset event, and it's at 19 o'clock (if we look at the hour distribution above, we'll see there's no onset events at that hour), the model will classifiy that point neither an onset event, nor a wakeup event.</p>\n<h1>The risk of overfitting</h1>\n<p>But we also have to be cautious of using hours, because since the test set is hidden, there's also can be some surprises, like samples which are really onset events, but because it's at 19 o'clock (for example), it'll be misclassified, in other words, it's <strong>overfitting</strong>. And there's many ways to avoid it. For example, we can use hours only in some epochs, or we can use hours only by some probability each time, and the probability can be scheduled over epochs and so on. <br>\n<strong>Note</strong>: <em>Not using hours means that we disable hours, for example, if we are using embeddings, we set all embedding values to zero, or set hours as NaN values, or add <code>-1</code> hour as a disabled hour and so on</em></p>\n<p>But if we are cautious of this sort of overfitting of using hours, we have to evaluate the model without using hours, and deploy it without using hours too.</p>\n<p>What do you think about it? Hope it was helpful!</p>\n<p>Good luck in the competition!</p>",
      "rawMarkdown": "Note: In this post, I'll be talking about neural networks\n\n# Introduction\n\nIn this competition we are given with series and events data and our task is to detect whether a child is sleeping or not by **break points**, but there are also some samples where a child doesn't wear the device, and that's the main problem of the competition. And it's not easy to detect, since the device is supposed to measure angle of an arm by fluctuation, and when the device is unweared, it tries to capture every single fluctuation, and that confuses our model\n\n# How hours can help us to deal with those samples?\nIf we see at the hour distribution showed below, we can see that there is no onset and wakeup events at some hours:\n![hour distribution](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8913839%2F93ec145f17edce432e7f526ea66c411d%2Fdownload.png?generation=1700753722980321&alt=media)\nSo, for example, if there is a pattern, which seems to show an onset event, and it's at 19 o'clock (if we look at the hour distribution above, we'll see there's no onset events at that hour), the model will classifiy that point neither an onset event, nor a wakeup event.\n\n# The risk of overfitting\n\nBut we also have to be cautious of using hours, because since the test set is hidden, there's also can be some surprises, like samples which are really onset events, but because it's at 19 o'clock (for example), it'll be misclassified, in other words, it's **overfitting**. And there's many ways to avoid it. For example, we can use hours only in some epochs, or we can use hours only by some probability each time, and the probability can be scheduled over epochs and so on. \n**Note**: *Not using hours means that we disable hours, for example, if we are using embeddings, we set all embedding values to zero, or set hours as NaN values, or add `-1` hour as a disabled hour and so on*\n\nBut if we are cautious of this sort of overfitting of using hours, we have to evaluate the model without using hours, and deploy it without using hours too.\n\nWhat do you think about it? Hope it was helpful!\n\nGood luck in the competition!",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2535803": "Note: In this post, I'll be talking about neural networks\n\n# Introduction\n\nIn this competition we are given with series and events data and our task is to detect whether a child is sleeping or not by **break points**, but there are also some samples where a child doesn't wear the device, and that's the main problem of the competition. And it's not easy to detect, since the device is supposed to measure angle of an arm by fluctuation, and when the device is unweared, it tries to capture every single fluctuation, and that confuses our model\n\n# How hours can help us to deal with those samples?\nIf we see at the hour distribution showed below, we can see that there is no onset and wakeup events at some hours:\n![hour distribution](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8913839%2F93ec145f17edce432e7f526ea66c411d%2Fdownload.png?generation=1700753722980321&alt=media)\nSo, for example, if there is a pattern, which seems to show an onset event, and it's at 19 o'clock (if we look at the hour distribution above, we'll see there's no onset events at that hour), the model will classifiy that point neither an onset event, nor a wakeup event.\n\n# The risk of overfitting\n\nBut we also have to be cautious of using hours, because since the test set is hidden, there's also can be some surprises, like samples which are really onset events, but because it's at 19 o'clock (for example), it'll be misclassified, in other words, it's **overfitting**. And there's many ways to avoid it. For example, we can use hours only in some epochs, or we can use hours only by some probability each time, and the probability can be scheduled over epochs and so on. \n**Note**: *Not using hours means that we disable hours, for example, if we are using embeddings, we set all embedding values to zero, or set hours as NaN values, or add `-1` hour as a disabled hour and so on*\n\nBut if we are cautious of this sort of overfitting of using hours, we have to evaluate the model without using hours, and deploy it without using hours too.\n\nWhat do you think about it? Hope it was helpful!\n\nGood luck in the competition!"
  }
}