{
  "fetched_at": "2026-10-25T07:47:15Z",
  "source_url": "https://www.kaggle.com/competitions/cad-2020-cncer-de-mama/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2>Atributos</h2>\n\n    - SampleId: Código que identifica cada muestra\n    -  ClumpThickness: Espesor de la masa. Se mide como un entero entre 1 – 103.\n    -  Uniformity of CellSize: Uniformidad del tamaño de la célula. Se mide como un entero entre 1 – 104.\n    -  Uniformity of CellShape: Uniformidad de la forma de la célula. Se mide como un entero entre 1 – 105.\n    -  Marginal Adhesion: Adhesión marginal. Se mide como un entero entre 1 – 106.\n    -  Single EpithelialCellSize:Tamaño individual de la célula, entero entre 1 – 107.\n    -  BareNuclei: Núcleo, entero entre 1 – 108.\n    -  BlandChromatin: Cromatina Blanda. Se mide como un entero entre 1 – 109.\n    -  Normal Nucleoli: Nucleoli normal. Se mide como un entero entre 1 – 1010.\n    -  Mitoses: Mitosis. Se mide como un entero entre 1 – 1011.\n    -  Class: Malignidad 2: benigno, 4: maligno\n",
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    {
      "name": "Description",
      "content": "<p>Base de datos - Wisconsin Breast Cancer Database</p>\n<p>Su fuente procede del Dr. WIlliam H. Wolberg; </p>\n<p>Cuando un patólogo examina una muestra de tejido en el diagnóstico del cáncer de mama se tienen presente nueve atributos que hablan de la forma, tamaño y malignidad del tumor.  Los atributos en la base de datos Wisconsin Breast Cancer Database son los siguientes</p>\n\n- SampleId: Código que identifica cada muestra\n- ClumpThickness: Espesor de la masa. Se mide como un entero entre 1 – 103. \n- UniformityCellSize: Uniformidad del tamaño de la célula. Se mide como un entero entre 1 – 104. \n- UniformityCellShape: Uniformidad de la forma de la célula. Se mide como un entero entre 1 – 105. \n- MarginalAdhesion: Adhesión marginal. Se mide como un entero entre 1 – 106. \n-  SingleEpithelialCellSize:Tamaño individual de la célula, entero entre 1 – 107. \n- BareNuclei: Núcleo, entero entre 1 – 108. \n- BlandChromatin: Cromatina Blanda.  Se mide como un entero entre 1 – 109. \n- NormalNucleoli: Nucleoli normal.  Se mide como un entero entre 1 – 1010. \n- Mitoses: Mitosis. Se mide como un entero entre 1 – 1011. \n- Class: Malignidad 2: benigno, 4: maligno\n</p>\n\n<p> El objetivo es predicir la clase de tumor que viene representada por el atributo Class </p>",
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    },
    {
      "name": "Evaluation",
      "content": "<p>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:</p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. 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:</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<p>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.</p>\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, 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.&nbsp;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).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
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      "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 - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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}