{
  "id": 1712,
  "title": "svm question (newbie question)",
  "url": "/competitions/WhatDoYouKnow/discussion/1712",
  "author_name": "",
  "post_date": "2012-04-12T03:42:15.830Z",
  "votes": null,
  "comment_count": 1,
  "views": 3332,
  "content": "<p>I was wondering why I haven't heard anyone use an SVM for this challenge.</p>\r\n<p>I tried using one on this dataset but didn't get good results (actually worse than benchmark).</p>\r\n<p>I was wondering if anyone could explain why this is? I understand the data doesn't become linearly seperable by any of the common kernel tricks.</p>\r\n<p>But why would performance be worse after doing a kernel trick? could anyone explain a bit of the theory here.</p>\r\n<p>Thank you.</p>",
  "messages": [
    {
      "id": "10079",
      "postDate": "04/12/2012 03:42:15",
      "content": "<p>I was wondering why I haven't heard anyone use an SVM for this challenge.</p>\r\n<p>I tried using one on this dataset but didn't get good results (actually worse than benchmark).</p>\r\n<p>I was wondering if anyone could explain why this is? I understand the data doesn't become linearly seperable by any of the common kernel tricks.</p>\r\n<p>But why would performance be worse after doing a kernel trick? could anyone explain a bit of the theory here.</p>\r\n<p>Thank you.</p>",
      "rawMarkdown": "",
      "votes": null
    },
    {
      "id": "10428",
      "postDate": "04/27/2012 04:24:08",
      "content": "<p>morenoh149, the problem seems to be that we are predicting new data, rather than describing the current data. The new data differs from the current data in subtle ways. So the prediction mechanism needs to accommodate these subtle differences. This is difficult\r\n to achieve. It appears that even the winning entry was only marginally successful.</p>\r\n<p>Steffen Rendle has explained his winning methodology (with a mention of SVM) at\r\n<a title=\"http://blog.kaggle.com/2012/04/20/viva-libfm-steffen-rendle-on-how-he-won-the-grockit-challenge/\" href=\"http://blog.kaggle.com/2012/04/20/viva-libfm-steffen-rendle-on-how-he-won-the-grockit-challenge/\">\r\nhttp://blog.kaggle.com/2012/04/20/viva-libfm-steffen-rendle-on-how-he-won-the-grockit-challenge/</a></p>",
      "rawMarkdown": "",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 10428,
      "author_name": "mikel1",
      "author_url": "",
      "post_date": "04/27/2012 04:24:08",
      "content": "<p>morenoh149, the problem seems to be that we are predicting new data, rather than describing the current data. The new data differs from the current data in subtle ways. So the prediction mechanism needs to accommodate these subtle differences. This is difficult\r\n to achieve. It appears that even the winning entry was only marginally successful.</p>\r\n<p>Steffen Rendle has explained his winning methodology (with a mention of SVM) at\r\n<a title=\"http://blog.kaggle.com/2012/04/20/viva-libfm-steffen-rendle-on-how-he-won-the-grockit-challenge/\" href=\"http://blog.kaggle.com/2012/04/20/viva-libfm-steffen-rendle-on-how-he-won-the-grockit-challenge/\">\r\nhttp://blog.kaggle.com/2012/04/20/viva-libfm-steffen-rendle-on-how-he-won-the-grockit-challenge/</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "10079": "",
    "10428": ""
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  "source": "meta"
}