{
  "id": 372918,
  "title": "What does this Graph mean?",
  "url": "/competitions/rsna-breast-cancer-detection/discussion/372918",
  "author_name": "Akmal Azzam o'g'li",
  "post_date": "2022-12-18T16:56:22.837000",
  "votes": 0,
  "comment_count": 4,
  "views": 0,
  "content": "<p>Hi Kaggler Professionals,</p>\n<p>Could you be kind to give your opinion on this to model history graphs, what does they mean?<br>\nHow i could improve model?</p>\n<p><a href=\"https://postimg.cc/2qc9JBzv\" target=\"_blank\"><img src=\"https://i.postimg.cc/2qc9JBzv/download.png\" alt=\"download\"></a></p>\n<p><a href=\"https://postimg.cc/0MKnBWXR\" target=\"_blank\"><img src=\"https://i.postimg.cc/0MKnBWXR/download-1.png\" alt=\"download-1\"></a></p>\n<p>Best regards,<br>\nAkmal</p>",
  "messages": [
    {
      "id": 2069232,
      "postDate": "2022-12-18T18:39:27.223Z",
      "content": "<p>Looks like overfitting to me, the loss is the strong indicator which overweights in this case (especially when using Prob-F1)<br>\nVal loss sharply going up while train loss goes down.</p>\n<p>Things you can do against overfitting:</p>\n<ul>\n<li>reduce model complexity (less parameters, less overfitting)</li>\n<li>use regularization methods (e.g. L1 regularization, L2 regularization)</li>\n<li>add Dropout layers</li>\n<li>add BatchNormalization layers</li>\n<li>try data augmentation methods to increase data size or try to research public available data (with open license)</li>\n<li>use label smoothing to generalize better</li>\n<li>reduce data dimensionality for less variance but higher bias</li>\n<li>use early stopping to detect overfitting</li>\n<li>fight against the data imbalance in this dataset by using special losses, methods etc. (e.g. FocalLoss, SMOTE, …)</li>\n<li>Make sure you have no noisy data so that your model does not learn the noise patterns</li>\n</ul>\n<p>and many more</p>",
      "rawMarkdown": "Looks like overfitting to me, the loss is the strong indicator which overweights in this case (especially when using Prob-F1)\nVal loss sharply going up while train loss goes down.\n\nThings you can do against overfitting:\n\n- reduce model complexity (less parameters, less overfitting)\n- use regularization methods (e.g. L1 regularization, L2 regularization)\n- add Dropout layers\n- add BatchNormalization layers\n- try data augmentation methods to increase data size or try to research public available data (with open license)\n- use label smoothing to generalize better\n- reduce data dimensionality for less variance but higher bias\n- use early stopping to detect overfitting\n- fight against the data imbalance in this dataset by using special losses, methods etc. (e.g. FocalLoss, SMOTE, ...)\n- Make sure you have no noisy data so that your model does not learn the noise patterns\n\nand many more",
      "votes": 2,
      "replies": [
        {
          "id": 2069301,
          "postDate": "2022-12-18T20:41:27.113Z",
          "content": "<p><a href=\"https://www.kaggle.com/aliabdin1\" target=\"_blank\">@aliabdin1</a> Thank you for your detailed answer, i will try to follow.</p>",
          "rawMarkdown": "@aliabdin1 Thank you for your detailed answer, i will try to follow."
        }
      ]
    },
    {
      "id": 2069394,
      "postDate": "2022-12-18T22:44:07.283Z",
      "content": "<p>what is the threshold used to plot f1 graphs?</p>",
      "rawMarkdown": "what is the threshold used to plot f1 graphs?",
      "replies": [
        {
          "id": 2070029,
          "postDate": "2022-12-19T14:45:28.723Z",
          "content": "<p><a href=\"https://www.kaggle.com/hengck23\" target=\"_blank\">@hengck23</a> if i understand right it is 0.5</p>",
          "rawMarkdown": "@hengck23 if i understand right it is 0.5"
        }
      ]
    },
    {
      "id": 2069165,
      "postDate": "2022-12-18T16:56:22.837Z",
      "content": "<p>Hi Kaggler Professionals,</p>\n<p>Could you be kind to give your opinion on this to model history graphs, what does they mean?<br>\nHow i could improve model?</p>\n<p><a href=\"https://postimg.cc/2qc9JBzv\" target=\"_blank\"><img src=\"https://i.postimg.cc/2qc9JBzv/download.png\" alt=\"download\"></a></p>\n<p><a href=\"https://postimg.cc/0MKnBWXR\" target=\"_blank\"><img src=\"https://i.postimg.cc/0MKnBWXR/download-1.png\" alt=\"download-1\"></a></p>\n<p>Best regards,<br>\nAkmal</p>",
      "rawMarkdown": "Hi Kaggler Professionals,\n\nCould you be kind to give your opinion on this to model history graphs, what does they mean?\nHow i could improve model?\n\n<a href='https://postimg.cc/2qc9JBzv' target='_blank'><img src='https://i.postimg.cc/2qc9JBzv/download.png' border='0' alt='download'/></a>\n\n<a href='https://postimg.cc/0MKnBWXR' target='_blank'><img src='https://i.postimg.cc/0MKnBWXR/download-1.png' border='0' alt='download-1'/></a>\n\nBest regards,\nAkmal\n"
    }
  ],
  "comments": [
    {
      "id": 2069232,
      "author_name": "Ali Abdin",
      "author_url": "",
      "post_date": "2022-12-18T18:39:27.223000",
      "content": "<p>Looks like overfitting to me, the loss is the strong indicator which overweights in this case (especially when using Prob-F1)<br>\nVal loss sharply going up while train loss goes down.</p>\n<p>Things you can do against overfitting:</p>\n<ul>\n<li>reduce model complexity (less parameters, less overfitting)</li>\n<li>use regularization methods (e.g. L1 regularization, L2 regularization)</li>\n<li>add Dropout layers</li>\n<li>add BatchNormalization layers</li>\n<li>try data augmentation methods to increase data size or try to research public available data (with open license)</li>\n<li>use label smoothing to generalize better</li>\n<li>reduce data dimensionality for less variance but higher bias</li>\n<li>use early stopping to detect overfitting</li>\n<li>fight against the data imbalance in this dataset by using special losses, methods etc. (e.g. FocalLoss, SMOTE, …)</li>\n<li>Make sure you have no noisy data so that your model does not learn the noise patterns</li>\n</ul>\n<p>and many more</p>",
      "votes": 2,
      "replies": [
        {
          "id": 2069301,
          "author_name": "Akmal Azzam o'g'li",
          "author_url": "",
          "post_date": "2022-12-18T20:41:27.113000",
          "content": "<p><a href=\"https://www.kaggle.com/aliabdin1\" target=\"_blank\">@aliabdin1</a> Thank you for your detailed answer, i will try to follow.</p>",
          "votes": 0,
          "replies": []
        }
      ]
    },
    {
      "id": 2069394,
      "author_name": "hengck23",
      "author_url": "",
      "post_date": "2022-12-18T22:44:07.283000",
      "content": "<p>what is the threshold used to plot f1 graphs?</p>",
      "votes": 0,
      "replies": [
        {
          "id": 2070029,
          "author_name": "Akmal Azzam o'g'li",
          "author_url": "",
          "post_date": "2022-12-19T14:45:28.723000",
          "content": "<p><a href=\"https://www.kaggle.com/hengck23\" target=\"_blank\">@hengck23</a> if i understand right it is 0.5</p>",
          "votes": 0,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2069232": "Looks like overfitting to me, the loss is the strong indicator which overweights in this case (especially when using Prob-F1)\nVal loss sharply going up while train loss goes down.\n\nThings you can do against overfitting:\n\n- reduce model complexity (less parameters, less overfitting)\n- use regularization methods (e.g. L1 regularization, L2 regularization)\n- add Dropout layers\n- add BatchNormalization layers\n- try data augmentation methods to increase data size or try to research public available data (with open license)\n- use label smoothing to generalize better\n- reduce data dimensionality for less variance but higher bias\n- use early stopping to detect overfitting\n- fight against the data imbalance in this dataset by using special losses, methods etc. (e.g. FocalLoss, SMOTE, ...)\n- Make sure you have no noisy data so that your model does not learn the noise patterns\n\nand many more",
    "2069394": "what is the threshold used to plot f1 graphs?",
    "2069165": "Hi Kaggler Professionals,\n\nCould you be kind to give your opinion on this to model history graphs, what does they mean?\nHow i could improve model?\n\n<a href='https://postimg.cc/2qc9JBzv' target='_blank'><img src='https://i.postimg.cc/2qc9JBzv/download.png' border='0' alt='download'/></a>\n\n<a href='https://postimg.cc/0MKnBWXR' target='_blank'><img src='https://i.postimg.cc/0MKnBWXR/download-1.png' border='0' alt='download-1'/></a>\n\nBest regards,\nAkmal\n"
  }
}