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  "fetched_at": "2026-10-20T04:05:50Z",
  "source_url": "https://www.kaggle.com/competitions/cse-601-project-3-fall-2020/data",
  "pages": [
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      "name": "data-description",
      "content": "<p>The dataset used in this competition is sampled from a real world dataset. There are two classes (0 and 1) in the dataset.  In the train_features.csv and test_features.csv, each row represents a data sample and each column represents a feature. The first column represents the sample id which is an integer, and the rest columns are features which are all continuous real numbers. The train_label.csv includes the corresponding labels for the training set. In train_label.csv, each row represents a sample where the first column is the sample id  and the second column is the ground truth label of that sample. The sample_Submission.csv file provides an example for your reference. When preparing your own submission files, please follow the format of this example file, otherwise you may have errors in the evaluation. <p>\n<h2>File descriptions</h2>\n<ul>\n<li><strong>train_feature.csv</strong> - the features of the training set</li>\n<li><strong>test_feature.csv</strong> - the features of the test set</li>\n<li><strong>train_label.csv</strong> - the labels of the training set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n",
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      "name": "Description",
      "content": "<p> Please follow the rules described in the project description file (Project3.pdf) which can be found in piazza:  Apply  various  tricks  on  top  of  any  classification algorithm discussed in class (including nearest neighbor, decision tree, Naïve Bayes, SVM,  logistic  regression,  bagging, AdaBoost,  random  forests)  and tune  parameters using   training   data.</p>\n<p>This competition will due on the 10:30 AM (EST), Dec. 3, 2020. Please use your UB mail to register. You need to form your team with at most three people. Each team can only have one register account. The number of submissions for each team is limited to five for each day (date and time are in UTC). So please start as early as possible.&lt;\\p&gt;</p>",
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      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is F1-Score. The F1- Score, commonly used in bianry classification, 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<h2>Submission Format</h2>\n<p><strong>For every sample in the dataset</strong>, submission files should contain two columns: id and label in each row.  These two columns should be separated by a comma.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>id,label<br>458,1<br>\n<p> You can find a sample submission file (sample_Submission.csv) in the Data page.\n</p><h2>Leader Board</h2>\n<p>The leader board includes two parts: one is public leader board which is visible to you and the other is private leader board. For this competition, we set all testing data as private leader board. So you can not see your score on public leader board. You must split your training data to hold out a validation dataset to prevent overfitting. You can choose two submissions for the evaluation for the private leader board. You can hand-select the eligible submissions, or will otherwise default to a random scoring submissions for the private leader board evaluation. **Your final results are determined by the results on the private leader board.**</p></pre>",
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    {
      "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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