{
  "fetched_at": "2026-10-19T15:18:19Z",
  "source_url": "https://www.kaggle.com/competitions/cs-43615361-flare-classification/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2> Data Description </h2>\n<p> Each row of x_train (and x_test) represents flares for 1 hour and y_train represents if there is any flare in next 30 minutes. </p>\n<p> x-val and corresponding y_val is also provided to check the performance of the model. </p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<h2>Goal</h2>\n<p> The goal of this competition is to predict the flare in next 30 minutes. </p>\n<p> Have fun :) </p>\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.</p>\n<h2>Submission Format</h2>\n<p>Submissions to the Kaggle leaderboard must take a specific form. Submissions should be a csv with two columns, \"ID\" and \"Prediction\" and 55822 rows corresponding to the test observations. Including the header row, there will be 55823 rows in the submission file. The \"ID\" is a unique numeric identifier from \"1\", \"2\", ... to \"55822\". The \"Prediction\" is your predicted flux for the test set.</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}