{ "fetched_at": "2026-10-19T13:21:34Z", "source_url": "https://www.kaggle.com/competitions/data-challenge-supervised-ml/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!", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "

File descriptions

\n
  • features_train.csv : Contains Features of Train Data\n
  • target_train.csv : Contains Classes of Train Data\n
  • features_test.csv : Contains Features of Test Data\n\n\n

    Each row in the ‘features_train’ and ‘features_test’ belongs to a ‘measurement’ and each column represents a ‘feature’. For each row in the ‘features_train’ you have a corresponding class label in ‘target_train’. You can consider the row-numbers as keys.

    \n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "

    \nThis Data Challenge is to put everything you learned as part of Supervised Machine Learning Module into practice. The dataset we will be using contains two CSV files 'features' and ‘target’.\n

    \n\n

    Each row in the ‘features’ belongs to a ‘measurement’ and each column represents a ‘feature’. For each row in the ‘features’ you have a corresponding class label in ‘target’. You can consider the row-numbers as keys.

    \n\n

    You will have to use data labelled as train for implementing the models. You have to submit the results that you obtain from the predictions of your Machine Learning models from the data labelled as test.\n

    \n\n

    Complete the following:

    \n\n1. Explore your data. Can you identify anything interesting that is worth noting from the data?\n2. Define a set of possible classifiers and show which one performs best. Keep in mind the problem of overfitting.\n3. Using feature selection try to reduce the number of features. In the dataset, you have about 120 features. Find the good ones for your classifier.\n\nKeep in mind that there is no best solution to the challenge. Show how you approach a problem, and the skills/methods you use in that approach.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "

    The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:

    \n
    \n

    The evaluation metric for this competition is Macro F1-Score. 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:

    \n

    \\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]

    \n

    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.

    \n

    Submission Format

    \n

    For every row in the dataset, submission files should contain two columns: Id and Predicted. Predicted should be a single value between 0 and 2 representing predicted Class.

    \n

    The file should contain a header and have the following format:

    \n
    Id,Predicted
    0,1
    1,2
    2,0
    3,1
    etc.
    \n\n

    Submit your CSV file in 'Submit Predictions' section.

    \n\n

    You can check your score in 'My Submissions' section and choose the one to make it Final Score by checking the 'Use for Final Score' checkbox.

    \n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }