{
  "fetched_at": "2026-10-20T10:41:20Z",
  "source_url": "https://www.kaggle.com/competitions/aist4010-spring2023-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",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\n\nPrecision is the ratio of true positives **TP** to all predicted positives **TP + FP**. Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\n\nThe 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.\n\n## Submission Format\n\n**For every author in the dataset**, submission files 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```",
      "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 - Try this assignment!\n - Test some classification algorithms you know!\n - Get familiar with Kaggle!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}