{
  "id": 11129,
  "title": "Our Solution",
  "url": "/competitions/seizure-prediction/discussion/11129",
  "author_name": "Drew Abbot",
  "post_date": "2014-12-04T01:25:48.763000",
  "votes": 6,
  "comment_count": 0,
  "views": 2109,
  "content": "<p>Summary:</p>\n<p>Our winning submission was a weighted average of three separate models: a Generalized Linear Model regression with Lasso or elastic net regularization (via MATLAB's lassoglm function), a Random Forest (via MATLAB's TreeBagger implementation), and a bagged set of linear Support Vector Machines (via Python's scikit-learn toolkit).</p>\n<p>Before merging as a team, we developed different feature sets for our models, but both sets were a combination of&nbsp;time- and frequency-domain information. &nbsp;For more detailed information, see our detailed report located under&nbsp;our GitHub repo here:</p>\n<p>https://github.com/drewabbot/kaggle-seizure-prediction/blob/master/report.md</p>",
  "messages": [
    {
      "id": 59513,
      "postDate": "2014-12-04T01:25:48.763Z",
      "content": "<p>Summary:</p>\n<p>Our winning submission was a weighted average of three separate models: a Generalized Linear Model regression with Lasso or elastic net regularization (via MATLAB's lassoglm function), a Random Forest (via MATLAB's TreeBagger implementation), and a bagged set of linear Support Vector Machines (via Python's scikit-learn toolkit).</p>\n<p>Before merging as a team, we developed different feature sets for our models, but both sets were a combination of&nbsp;time- and frequency-domain information. &nbsp;For more detailed information, see our detailed report located under&nbsp;our GitHub repo here:</p>\n<p>https://github.com/drewabbot/kaggle-seizure-prediction/blob/master/report.md</p>",
      "votes": 6
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "59513": ""
  }
}