{
  "fetched_at": "2026-10-20T10:41:07Z",
  "source_url": "https://www.kaggle.com/competitions/aist4010-spring2024-a0/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Here is the data\n\n## Files\n\n*   **X_train.csv** - the training set features\n*   **y_train.csv** - the training set labels\n*   **X_test.csv** - the test set features\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\nFeature Attribute Information: (4 dimensions)\n\n*   sepal length in cm\n*   sepal width in cm\n*   petal length in cm\n*   petal width in cm\n\nLabel Information:\n\n*   0  -- Iris Setosa\n*   1  -- Iris Versicolour\n*   2  -- Iris Virginica",
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      "name": "Description",
      "content": "This is a **non-grading** assignment for you to get familiar with the Kaggle environment. \nFor official assignments, half of them will be fixed-answer questions while Half of them will be held on the Kaggle platform as competitions.\nIn assignment 0, you need to deal with the IRIS classification problem. Try **any** algorithm you like, just beat the very low **baseline**!\nData can be downloaded from the 'Data' tab on this same page on Kaggle. \nRegarding submission, you need to submit **all your codes** (including data-processing, training,  prediction, etc.) to this Kaggle page.\nYou can click the \"New Notebook\" button to create a notebook from the 'Code' tab on this same page to write your codes or upload your local notebook.\nYour codes are expected to generate a '.csv' including your predictions on the X_test data. The F-1 score will be calculated by the output file.\nGood luck!",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is F1 score.  (Calculating code: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)\n\nThe F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is::\n\n    F1 = 2 * (precision * recall) / (precision + recall)\n\nFor the binary classification task, we use the macro average of the two F1 score (class 0 and class 1) as the final evaluation metric. (Calculate metrics for each label, and find their unweighted mean.)\n   \nExamples\n--------\n    >>> import pandas as pd\n    >>> row_id_column_name = \"id\"\n    >>> y_true = [0, 1, 2, 0, 1, 2]\n    >>> y_true = pd.DataFrame(y_true)\n    >>> y_true[\"id\"] = range(len(y_true))\n    >>> y_pred = [0, 2, 1, 0, 0, 1]\n    >>> y_pred = pd.DataFrame(y_pred)\n    >>> y_pred[\"id\"] = range(len(y_pred))\n    >>> score(y_true.copy(), y_pred.copy(), row_id_column_name, average='macro')\n    0.26...\n\n## Submission Format\n\nThe ubmission file should contain two columns: `Id` and `Predicted`.\n\nThe file should contain a header and have the following format:\n```\nId,Predicted\n0,1\n1,1\n2,0\n```",
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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 - Try this assignment!\n - Test some classification algorithms you know!\n - Get familiar with Kaggle!\n - Have fun!",
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