{
  "fetched_at": "2026-10-19T07:34:40Z",
  "source_url": "https://www.kaggle.com/competitions/11785-f19-hw2p2-classification/data",
  "pages": [
    {
      "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!",
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      "content": "<p>As mentioned in the part 2 handout, the <code>score.py</code> utility is:</p>\n<pre><code>from sklearn.metrics import roc_auc_score\nimport sys\nimport pandas as pd\n\ndef get_auc(y_true, y_score):\n    auc = roc_auc_score(y_true, y_score)\n    return auc\n\nif __name__ == '__main__':\n    args = sys.argv[1:]\n    if len(args) < 2:\n        print \"python score.py <score_csv> <label_csv>\"\n        exit()\n    score_csv = args[0]\n    label_csv = args[1]\n    y_score = pd.read_csv(score_csv).score.values\n    y_true = pd.read_csv(label_csv).score.values\n    auc = get_auc(y_true, y_score)\n    print \"AUC: \", auc</code></pre>",
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      "name": "Description",
      "content": "<h1>Face Classification</h1> \n<p>Given a face image as input to your system, you have to predict the ID of the person's face. The true face image ID will be present in the training data and so the network will be doing an N-way classification to get the prediction. You are provided with the development set and can fine-tune the model based on the accuracy you get on the development set.</p>",
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    {
      "name": "Evaluation",
      "content": "<h1> Evaluation Metric </h1>\nThe evaluation metric for this competition is the classification accuracy.\n\n<h1> Submission Form </h1>\n<p>You will submit a csv file created form the test_order_classification provided to you. The first column will be the same as the one in the file and the second column will be the class ID you predict for the given test image.</p>",
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