{ "fetched_at": "2026-10-24T10:53:12Z", "source_url": "https://www.kaggle.com/competitions/datachalage101mfab/data", "pages": [ { "name": "rules", "content": "\r\n\t\r\n

One account per participant

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You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.

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No private sharing outside teams

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\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n

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Team Mergers

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Team mergers are not allowed in this competition.

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Team Limits

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There is no maximum team size.

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Submission Limits

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You may submit a maximum of 2 entries per day.

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You may select up to 2 final submissions for judging.

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Competition Timeline

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Start Date: TBA

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Merger Deadline: None

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Entry Deadline: None

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End Date: 2/1/2014 12:00 AM UTC

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The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "

Provided data set consist of 3 main parts.

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Design parameters

Expected design parameters and other fixed fabric properties for every design are collected in this data set and it divided in to two so that you can use Design-train.csv for model training and Design-test.csv for predicting sample result.

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Yarn consumption

- Yarn consumption and properties of yarn used. There are 3 main properties for each yarn type-design combination,\n
  • M per repeat – how many meters of yarn consumed to knit one design repeat
    • \n
  • Yarn count – Yarn width
    • \n
  • Percentage - Yarn consumption(m) as a percentage of total yarn consumption for one repeat
    • \nThis data set also divided in to two for training and testing.\n

    Result

    – Resulted design parameter under different tension rate settings. This data set map deferent machine tension rates (FRJ, FRP, FRL) to corresponding resulted design parameters (PRL, PRL, S) .\nDesingID is the key to relate all the data sets \n\n\n

    Data fields

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

    MFAB Data Challenge 101

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    In knitted fabric manufacturing industry setting up knitting machine with correct machine parameters is one of challenging task. specially in design fabric manufacturing there are more design parameters to measure in final output fabric. Main design parameters we are interested in this challenge are,

    \n\n\n
  • Pattern Repeat Length (PRL)
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  • Pattern Repeat Width (FRW)
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  • Stretch (S)
    • \n\n

    Further some fabric properties are fixed at designing stage of the fabric. Those are

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  • \tKnit per CM
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  • Needle setup
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  • Machine type
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  • Number of bands
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    In addition to that properties of the fabric heavily depend on the row material (yarns) use to knit the fabric. Yarn consumption and properties of any fabric have defined under 41 yarn types.\nIn order to produced fabric with expected design parameters there are 3 yarn tension rates should set correctly.

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  • Feed Rate Jarcard (FRJ)
    • \t\n
  • Feed Rate Pillar (FRP)
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  • Feed Rate Lycra (FRL)
    • \n

    Here we challenge you to build up a model to predict the correct yarn tension rate by using expected design parameters and yarn consumption data.\n

    \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:

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    \r\n

    The evaluation metric for this competition is Mean 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:

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

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    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.

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    Submission Format

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    For every author in the dataset, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).

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

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    AuthorId,DuplicateAuthorIds
    1,1
    8,8
    9,9 10
    10,10 9
    etc.
    ", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }