{
  "id": 114380,
  "title": "Past Kaggle competitions with RMSLE metric",
  "url": "/competitions/ashrae-energy-prediction/discussion/114380",
  "author_name": "",
  "post_date": "2019-10-25T21:34:02.774830800Z",
  "votes": 22,
  "comment_count": 2,
  "views": 0,
  "content": "<p>In <strong>ASHRAE</strong> competition, <a href=\"https://www.kaggle.com/c/ashrae-energy-prediction/overview/evaluation\">The Root Mean Squared Log Error (RMSLE)</a> metric is used:</p>\n\n<p>The RMSLE is calculated as:</p>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2Fff4f21b4458b45a23de700c3c9ade596%2FScreenshot%20from%202019-10-25%20232944.png?generation=1572039014994602&amp;alt=media\" alt=\"\"></p>\n\n<p><strong>A</strong> <a href=\"https://www.kaggle.com/carlolepelaars/understanding-the-metric-rmsle\"><strong>nice kernel about RMSLE</strong></a> by <strong>Carlo Lepelaars</strong> @carlolepelaars <strong>Please have a look! And upvote it.</strong></p>\n\n<p><strong>See also</strong> <a href=\"https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a\">RMSE vs RMSLE</a></p>\n\n<p>Below are <strong>past Kaggle competitions evaluated on the RMSLE</strong> metric. \nIt <strong>might useful</strong> to refresh something, compare, etc.</p>\n\n<ul>\n<li><p>2018 <a href=\"https://www.kaggle.com/c/santander-value-prediction-challenge\">Santander Value Prediction Challenge</a>:\nPredict the value of transactions for potential customers.</p></li>\n<li><p>2018 <a href=\"https://www.kaggle.com/c/mercari-price-suggestion-challenge\">Mercari Price Suggestion Challenge</a>:\nCan you automatically suggest product prices to online sellers?</p></li>\n<li><p>2018 <a href=\"https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting\">Recruit Restaurant Visitor Forecasting</a>:\nPredict how many future visitors a restaurant will receive</p></li>\n<li><p>2017 <a href=\"https://www.kaggle.com/c/sberbank-russian-housing-market\">Sberbank Russian Housing Market</a>:\nCan you predict realty price fluctuations in Russia’s volatile economy?</p></li>\n<li><p>2016 <a href=\"https://www.kaggle.com/c/grupo-bimbo-inventory-demand\">Grupo Bimbo Inventory Demand</a>:\nMaximize sales and minimize returns of bakery goods </p></li>\n</ul>\n\n<p>A quick refresh of metrics can be found <a href=\"https://towardsdatascience.com/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-2-regression-metrics-d4a1a9ba3d74\">here</a>.</p>",
  "messages": [
    {
      "id": "658279",
      "postDate": "10/25/2019 21:34:02",
      "content": "<p>In <strong>ASHRAE</strong> competition, <a href=\"https://www.kaggle.com/c/ashrae-energy-prediction/overview/evaluation\">The Root Mean Squared Log Error (RMSLE)</a> metric is used:</p>\n\n<p>The RMSLE is calculated as:</p>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2Fff4f21b4458b45a23de700c3c9ade596%2FScreenshot%20from%202019-10-25%20232944.png?generation=1572039014994602&amp;alt=media\" alt=\"\"></p>\n\n<p><strong>A</strong> <a href=\"https://www.kaggle.com/carlolepelaars/understanding-the-metric-rmsle\"><strong>nice kernel about RMSLE</strong></a> by <strong>Carlo Lepelaars</strong> @carlolepelaars <strong>Please have a look! And upvote it.</strong></p>\n\n<p><strong>See also</strong> <a href=\"https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a\">RMSE vs RMSLE</a></p>\n\n<p>Below are <strong>past Kaggle competitions evaluated on the RMSLE</strong> metric. \nIt <strong>might useful</strong> to refresh something, compare, etc.</p>\n\n<ul>\n<li><p>2018 <a href=\"https://www.kaggle.com/c/santander-value-prediction-challenge\">Santander Value Prediction Challenge</a>:\nPredict the value of transactions for potential customers.</p></li>\n<li><p>2018 <a href=\"https://www.kaggle.com/c/mercari-price-suggestion-challenge\">Mercari Price Suggestion Challenge</a>:\nCan you automatically suggest product prices to online sellers?</p></li>\n<li><p>2018 <a href=\"https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting\">Recruit Restaurant Visitor Forecasting</a>:\nPredict how many future visitors a restaurant will receive</p></li>\n<li><p>2017 <a href=\"https://www.kaggle.com/c/sberbank-russian-housing-market\">Sberbank Russian Housing Market</a>:\nCan you predict realty price fluctuations in Russia’s volatile economy?</p></li>\n<li><p>2016 <a href=\"https://www.kaggle.com/c/grupo-bimbo-inventory-demand\">Grupo Bimbo Inventory Demand</a>:\nMaximize sales and minimize returns of bakery goods </p></li>\n</ul>\n\n<p>A quick refresh of metrics can be found <a href=\"https://towardsdatascience.com/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-2-regression-metrics-d4a1a9ba3d74\">here</a>.</p>",
      "rawMarkdown": "In **ASHRAE** competition, [The Root Mean Squared Log Error (RMSLE)](https://www.kaggle.com/c/ashrae-energy-prediction/overview/evaluation) metric is used:\n\nThe RMSLE is calculated as:\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2Fff4f21b4458b45a23de700c3c9ade596%2FScreenshot%20from%202019-10-25%20232944.png?generation=1572039014994602&amp;alt=media)\n\n\n**A** [**nice kernel about RMSLE**](https://www.kaggle.com/carlolepelaars/understanding-the-metric-rmsle) by **Carlo Lepelaars** @carlolepelaars **Please have a look! And upvote it.**\n\n**See also** [RMSE vs RMSLE](https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a)\n\nBelow are **past Kaggle competitions evaluated on the RMSLE** metric. \nIt **might useful** to refresh something, compare, etc.\n\n- 2018 [Santander Value Prediction Challenge](https://www.kaggle.com/c/santander-value-prediction-challenge):\nPredict the value of transactions for potential customers.\n\n- 2018 [Mercari Price Suggestion Challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge):\nCan you automatically suggest product prices to online sellers?\n\n- 2018 [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting):\nPredict how many future visitors a restaurant will receive\n\n- 2017 [Sberbank Russian Housing Market](https://www.kaggle.com/c/sberbank-russian-housing-market):\nCan you predict realty price fluctuations in Russia’s volatile economy?\n\n- 2016 [Grupo Bimbo Inventory Demand](https://www.kaggle.com/c/grupo-bimbo-inventory-demand):\nMaximize sales and minimize returns of bakery goods \n\n\nA quick refresh of metrics can be found [here](https://towardsdatascience.com/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-2-regression-metrics-d4a1a9ba3d74).",
      "votes": null
    },
    {
      "id": "658712",
      "postDate": "10/26/2019 12:13:15",
      "content": "<p>Thank you for mentioning my kernel! Our team will check out the past competitions to see if we can get some insights.</p>",
      "rawMarkdown": "Thank you for mentioning my kernel! Our team will check out the past competitions to see if we can get some insights.",
      "votes": null
    },
    {
      "id": "662977",
      "postDate": "11/01/2019 09:31:08",
      "content": "<p>Thanks for sharing.</p>",
      "rawMarkdown": "Thanks for sharing.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 658712,
      "author_name": "carlolepelaars",
      "author_url": "",
      "post_date": "10/26/2019 12:13:15",
      "content": "<p>Thank you for mentioning my kernel! Our team will check out the past competitions to see if we can get some insights.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 662977,
      "author_name": "longyin2",
      "author_url": "",
      "post_date": "11/01/2019 09:31:08",
      "content": "<p>Thanks for sharing.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "658279": "In **ASHRAE** competition, [The Root Mean Squared Log Error (RMSLE)](https://www.kaggle.com/c/ashrae-energy-prediction/overview/evaluation) metric is used:\n\nThe RMSLE is calculated as:\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1788308%2Fff4f21b4458b45a23de700c3c9ade596%2FScreenshot%20from%202019-10-25%20232944.png?generation=1572039014994602&amp;alt=media)\n\n\n**A** [**nice kernel about RMSLE**](https://www.kaggle.com/carlolepelaars/understanding-the-metric-rmsle) by **Carlo Lepelaars** @carlolepelaars **Please have a look! And upvote it.**\n\n**See also** [RMSE vs RMSLE](https://medium.com/analytics-vidhya/root-mean-square-log-error-rmse-vs-rmlse-935c6cc1802a)\n\nBelow are **past Kaggle competitions evaluated on the RMSLE** metric. \nIt **might useful** to refresh something, compare, etc.\n\n- 2018 [Santander Value Prediction Challenge](https://www.kaggle.com/c/santander-value-prediction-challenge):\nPredict the value of transactions for potential customers.\n\n- 2018 [Mercari Price Suggestion Challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge):\nCan you automatically suggest product prices to online sellers?\n\n- 2018 [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting):\nPredict how many future visitors a restaurant will receive\n\n- 2017 [Sberbank Russian Housing Market](https://www.kaggle.com/c/sberbank-russian-housing-market):\nCan you predict realty price fluctuations in Russia’s volatile economy?\n\n- 2016 [Grupo Bimbo Inventory Demand](https://www.kaggle.com/c/grupo-bimbo-inventory-demand):\nMaximize sales and minimize returns of bakery goods \n\n\nA quick refresh of metrics can be found [here](https://towardsdatascience.com/how-to-select-the-right-evaluation-metric-for-machine-learning-models-part-2-regression-metrics-d4a1a9ba3d74).",
    "658712": "Thank you for mentioning my kernel! Our team will check out the past competitions to see if we can get some insights.",
    "662977": "Thanks for sharing."
  },
  "source": "meta"
}