{
  "id": 265707,
  "title": "Are we given test labels, to check our results. ",
  "url": "/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/265707",
  "author_name": "",
  "post_date": "2021-08-16T16:09:57.736498900Z",
  "votes": 4,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Are we given test labels, to check our results. </p>",
  "messages": [
    {
      "id": "1475430",
      "postDate": "08/16/2021 16:09:57",
      "content": "<p>Are we given test labels, to check our results. </p>",
      "rawMarkdown": "Are we given test labels, to check our results.",
      "votes": null
    },
    {
      "id": "1475858",
      "postDate": "08/16/2021 21:31:09",
      "content": "<p>You can test your results by doing a K-fold cross validation <a href=\"https://machinelearningmastery.com/k-fold-cross-validation/\" target=\"_blank\">https://machinelearningmastery.com/k-fold-cross-validation/</a></p>\n<p>So just partition the training set into an 90-10 split (or whatever portion you want), run the model on this split with the 10% being your \"test\" set. Do this K times with a randomized split each time, or you could systematically divide the training data into K separate \"test\" sets, the remainder each time being your \"training set\" for that fold of cross validation.</p>",
      "rawMarkdown": "You can test your results by doing a K-fold cross validation https://machinelearningmastery.com/k-fold-cross-validation/\n\nSo just partition the training set into an 90-10 split (or whatever portion you want), run the model on this split with the 10% being your \"test\" set. Do this K times with a randomized split each time, or you could systematically divide the training data into K separate \"test\" sets, the remainder each time being your \"training set\" for that fold of cross validation.",
      "votes": null
    },
    {
      "id": "1476506",
      "postDate": "08/17/2021 06:26:38",
      "content": "<p>I am an idiot, but thanks sounds like an okay plan. </p>",
      "rawMarkdown": "I am an idiot, but thanks sounds like an okay plan.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1475858,
      "author_name": "d223chen",
      "author_url": "",
      "post_date": "08/16/2021 21:31:09",
      "content": "<p>You can test your results by doing a K-fold cross validation <a href=\"https://machinelearningmastery.com/k-fold-cross-validation/\" target=\"_blank\">https://machinelearningmastery.com/k-fold-cross-validation/</a></p>\n<p>So just partition the training set into an 90-10 split (or whatever portion you want), run the model on this split with the 10% being your \"test\" set. Do this K times with a randomized split each time, or you could systematically divide the training data into K separate \"test\" sets, the remainder each time being your \"training set\" for that fold of cross validation.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1476506,
      "author_name": "marcusrose",
      "author_url": "",
      "post_date": "08/17/2021 06:26:38",
      "content": "<p>I am an idiot, but thanks sounds like an okay plan. </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1475430": "Are we given test labels, to check our results.",
    "1475858": "You can test your results by doing a K-fold cross validation https://machinelearningmastery.com/k-fold-cross-validation/\n\nSo just partition the training set into an 90-10 split (or whatever portion you want), run the model on this split with the 10% being your \"test\" set. Do this K times with a randomized split each time, or you could systematically divide the training data into K separate \"test\" sets, the remainder each time being your \"training set\" for that fold of cross validation.",
    "1476506": "I am an idiot, but thanks sounds like an okay plan."
  },
  "source": "meta"
}