{ "source_file": "0930", "source_platform": "Medium", "author": "Ransaka Ravihara", "title_listed": "How to Evaluate Learning to Rank Models", "keywords": "LTR,评估指标", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/how-to-evaluate-learning-to-rank-models-d12cadb99d47/", "publish_date": "2023-01", "corpus_stage": "预训练/SFT", "notes": "", "id": "003814dc06b0", "collected_at": "2026-10-01T23:22:10", "title": "How to Evaluate Learning to Rank Models | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# How to Evaluate Learning to Rank Models\nA practical guide on how to evaluate LTR models in Machine Learning\n[Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\nJanuary 17, 20233 min read\n![Photo by Markus Spiske on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/0A8g25ACX9s2ZnCjl-scaled.jpg)\nPhoto by [Markus Spiske](https://unsplash.com/@markusspiske?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nMy previous article explained the three primary methodologies for approaching learning to Rank problems. In this article, we will be focused on how to evaluate an LTR model. Let's start.\n* * *\nWe have a few selections for evaluating the LTR model. However, these options vary from the approach we are using. We should use _**binary relevance**_ metrics if the goal is to assign a binary relevance score to each document. We should use _**graded relevance**_ if the goal is to set a relevance score for each document on a continuous scale. Let's discuss the widely used three types of evaluation matrices.\n**Mean Average Precision (MAP)**\nCalculating MAP for ranked results can be tricky and often confusing. Let's take a tiny step at a time shown below figure.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n![Steps of MAP | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/141YC0jvU4knjtiLULKx9ng.jpeg)\nSteps of MAP | Image by Author\nMAP has a few potential drawbacks,\n 1. It does not consider the ranking of the retrieved items, only the presence or absence of relevant documents.\n 2. It may not be appropriate for datasets where the relevance of items is not binary, as it does not consider an item's degree of relevance.\n\n\nWe will explore more on this later with examples.\n**Mean Reciprocal Rank (MRR)**\nA straightforward evaluation metric.\n![MRR Metric | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1v5_XaxUtOj6j9v2pJwtuxA.jpeg)\nMRR Metric | Image by Author\nOne potential drawback of MRR is it considers only the first relevant document for the given query.\n**Normalized Discounted Cumulative Gain (NDCG)**\nThis is the most common and ideal evaluation metric for most use cases. It accounts for both the relevance and the position of the results. It can assign a higher score for highly relevant results and appears at the top of the list.\n![Steps of NDCG | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1WZddJMjp6GMqg2iX3hNxiw.jpeg)\nSteps of NDCG | Image by Author\nIf you consider the DCG equation, the numerator increases when the document's relevance is high; the denominator increases when the position of the document increases. Altogether, DCG value will go high when highly relevant items are ranked higher.\n### Evaluating the LTR model by hand\nAssume we have two queries, _q1 and q2,_ and our LTR model returned below two document lists per each query, respectively. (_Note the order of documents)_\n_q1_ → _(d1,d2,d3)_q2 →_(d4,d5,d6)_\nFurthermore, based on the ground truth labels, we noticed that only _d1,d5,_ and _d6_ are relevant for the given two queries.\nLet's calculate each evaluation metric discussed above.\n * **Calculate the** _**MAP**_\n\n\nThe equation used here is,\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1rv29u7SolG5T-jC0a0Mg3g.png)\nImage by Author\nLet's substitute values related to query _q1,_\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1LNmTf2JZXQvAG6v4SSokZA.png)\nImage by Author\nAnd the same for _q2._\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1xB_Xi_HpSfEObKiJ-kbE7Q.png)\nImage by Author\nFinally, we can get the MAP for the given document- query pairs as shown below.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1rRpZJ8sUoJDuDAK2sOmsxw.png)\nImage by Author\nPlease note if you change the order of _d5_ and _d6,_ you still get the same MAP value for query _q2_ since this can't take rank into calculation.\n * **Calculate the** _**MAR**_\n\n\nWe can calculate _MAR_ as shown above. Not a complex equation.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1FMh8lPpUndi9Jkp3Uq3T3A.png)\nImage by Author\n * **Calculate the NDCG** (for query _q2)_\n\n\nThe equation to be used here is,\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1rU_SNlCjLIu5FZbrn0rjsA.png)\nImage by Author\n _Step 01: Calculate the DCG(q2)_\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1-JdhTwEP5xosm87NDLdvlQ.jpeg)\nImage by Author\n _Step 02: Calculate the IDCG(q2)_\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1Q6QuQI_a9H0ewRIBl5mBQw.jpeg)\nImage by Author\n _Step 03: Calculate the NDCG(q2)_\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1Z4SNTkC-MdMP20PXF5uOhw.png)\nImage by Author\n _Next in the Series:_[_How to Implement Learning to Rank Model using Python_](https://towardsdatascience.com/how-to-implement-learning-to-rank-model-using-python-569cd9c49b08)\n> [**How to Implement Learning to Rank Model using Python**](https://towardsdatascience.com/how-to-implement-learning-to-rank-model-using-python-569cd9c49b08)\n### Conclusion\nThere are other evaluation metrics for evaluating the LTR models. But here, I have explained widely used metrics. But choosing the right evaluation metric can often be tricky. Because it heavily depends on the dataset (mainly the target label) and the problem we are addressing.\nThank you for reading!\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657265628_qm714e.jpeg)\nWritten By\n## [Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[See all from Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Learning To Rank](https://towardsdatascience.com/tag/learning-to-rank/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Math](https://towardsdatascience.com/tag/math/)[Supervised Learning](https://towardsdatascience.com/tag/supervised-learning/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-evaluate-learning-to-rank-models-d12cadb99d47%2F&title=How%20to%20Evaluate%20Learning%20to%20Rank%20Models)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-evaluate-learning-to-rank-models-d12cadb99d47%2F&title=How%20to%20Evaluate%20Learning%20to%20Rank%20Models)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-evaluate-learning-to-rank-models-d12cadb99d47%2F&text=How%20to%20Evaluate%20Learning%20to%20Rank%20Models)\n\n\nTowards Data Science is a community publication. 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