{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "John Adeojo",
  "title_listed": "How to Avoid Being Fooled by Model Accuracy",
  "keywords": "",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/how-to-avoid-being-fooled-by-model-accuracy-e26307385fe1/",
  "publish_date": "2023-07-07",
  "corpus_stage": "",
  "notes": "",
  "id": "d7ab90784a04",
  "collected_at": "2026-10-02T07:04:42",
  "title": "How to Avoid Being Fooled by Model Accuracy | Towards Data Science",
  "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. 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 Avoid Being Fooled by Model Accuracy\nA visual guide to binary classification model metrics and their proper usage\n[John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\nJuly 7, 20238 min read\n![](../images/000.png)\n## Background - Simple on the Surface\nThe metrics used for gauging performance of classification models are fairly straightforward, at least from a mathematical standpoint. Nevertheless, I have observed that many modellers and data scientists encounter difficulty articulating these metrics, and even apply them incorrectly. This is an easy mistake to make, as these metrics appear simple on the surface, yet their implications can be profound depending on the problem domain.\nThis article serves as a visual guide to explaining common classification model metrics. We will explore definitions and use examples to highlight where metrics are used inappropriately.\n### A Brief Note on Visualisation\nEach visualisation comprises of ninety subjects, representing anything we might wish to classify. Blue subjects denote negative samples, whilst red are positive samples. The purple box is the model which attempts to predict positive samples. Anything inside this box is what the model predicts as positive.\nWith that clarified, let's delve into the definitions.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n* * *\n## Precision & Recall\nFor many classification tasks there is a trade-off between precision and recall. It's frequently the case that optimising for recall incurs a cost to precision. But what do these terms actually mean? Let's begin with the mathematical definitions, and then move onto the visual representations.\n> Precision = TP/ (TP + FP)\n> Recall = TP/(TP + FN)\n_Where TP = Number of true positives, FP = Number of false positives, FN = Number of false negatives._\nLet's focus on the chart directly below in which there are four positive samples. Remember, the model's positive predictions are represented by the box on the chart. Observing the chart, we see the model correctly predicts all four positive samples— we can see this as all the positive samples sit within the box. We can calculate model recall from the chart by counting positive cases within the box (TP = 4) divided by the total number of positive cases (TP = 4 + FN = 0).\n_Note FN is 0 because there are no positive cases outside of the box._\nPrecision can be explained similarly. It is simply the number of positive cases in the box (TP=4) divided by the total number of cases in the box (TP = 4 + FP = 6). A straightforward calculation reveals the model's precision to be just 40%.\nYou can observe that a model can have a high recall but low precision, and vice versa. The chart below shows this, where recall is just 50%, while precision is 100%. See if you can internalise how to get to these numbers.\n_Here is a clue to help you, the number of false negatives is two since there are two positive samples outside of the box._\n* * *\n## False Positive Rates & True Negative Rates\nThe false positive rate (FPR) perhaps appears more intuitive, possibly due to its name. However, let's explore the concept in the same way we did for the other metrics. Mathematically, we express the FPR as follows:\n> FPR = FP/(FP + TN)\n_Here, TN represents the number of true negative subjects._\nExamining the first image again, the FPR can be determined by looking at the number of negative samples inside the box (FP=6) divided by the total number of negative samples (FP=6 + TN=80). For our first image, the false positive rate is just 7%, and for the second, it's 0%. Try figuring out why this is the case.\n_Remember, the subjects inside the box are those that the model_ _**predicts**_ _are positive. So by extension, the negative samples outside the box are those that the model has identified as negative._\nThe true negative rate (TNR) can be computed using the following formula:\n> TNR = TN/(TN + FP)\n_Notice that TNR is always one minus the false positive rate._\n* * *\n## Accuracy\nAccuracy is a term that is loosely thrown around in the context of model performance, but what does it actually mean? Let's start with the mathematical definition:\n> Accuracy = (TP + TN) / (TP + TN + FP + FN)\nUsing the same logic we previously applied, we can calculate the model's accuracy as 93% for the first image and 97% for the second (see if you can derive this for yourself). This might be raising red flags in your mind as to why accuracy can be a deceptive metric in some cases. We will explore this in greater detail next.\n* * *\n## Using Metrics Correctly\nWhy do we concern ourselves with these metrics? Because they equip us with ways to assess the performance of our models. Once we comprehend these metrics, we can even determine the commercial value associated with models. This is why it is important to have a good intuition around there appropriate (and inappropriate) use. To illustrate this, we will briefly investigate the two common scenarios in classification tasks, namely balanced and imbalanced datasets.\n* * *\n## Imbalanced Datasets\nThe diagrams depicted earlier are instances of imbalanced classification tasks. Put simply, imbalanced tasks have a low representation of positive subjects compared with negative subjects. Many commercial use cases for binary classification fall into this category like credit card fraud detection and customer churn prediction, spam filtering etc. Selecting the incorrect metrics for imbalanced classification can lead you to have over-optimistic beliefs about the performance of your model.\nThe primary issue with imbalanced classification is the potential for the number of true negative samples to be high, and false negatives to be low. To illustrate, let's consider another model and assess it on our imbalanced data. We can create an extreme scenario where the model simply predicts every subject as negative.\nLet's compute each of the metrics in this scenario.\n  * **Accuracy** : _(TP=0 + TN=86)/(TP=0 + TN=86 + FP=0 + FN=4) = 95%_\n  * **Precision** : _(TP=0) /(TP=0 + FP=0) = undefined_\n  * **Recall** : _(TP=0) / (TP=0 + FN=4) = 0%_\n  * **FPR** : _(FP=0) / (FP=0 + TN=86) = 0%_\n  * **TNR** : _(TN=86) / (TN=86 + FP=0) = 100%_\n\n\nThe issues with accuracy, FPR and TNR should start to become more apparent. When we are working with imbalanced datasets, we can produce a high-accuracy model that performs poorly upon deployment. In the previous example, the model has no capacity to detect positive subjects but still achieves an accuracy of 95%, A 0% FPR and a perfect TNR.\nNow, imagine deploying such a model to conduct medical diagnostics or detect fraud; it would quite evidently be useless and perhaps even dangerous. This extreme example illustrates the problem of using metrics such as accuracy, FPR, and TPR to assess the performance of models working on imbalanced data.\n* * *\n## Balanced Datasets\nFor balanced classification problems, the number of potential true negatives is significantly smaller than in the imbalanced case.\nIf we take our \"non-discriminatory\" model and apply it to the balanced case, we obtain the following results:\n  * **Accuracy** : _(TP=0 + TN=45) / (TP=0 + TN=45 + FP=0 + FN=45) = 50%_\n  * **Precision** : _(TP=0) / (TP=0 + FP=0) = undefined_\n  * **Recall** : _(TP=0) / (TP=0 + FN=45) = 0%_\n  * **FPR** : _(FP=0) / (FP=0 + TN=45) = 0%_\n  * **TNR** : _(TN=45) / (TN=45 + FP=0) = 100%_\n\n\nWhile all of the other metrics remain the same, the model's accuracy has declined to 50%, arguably a much more indicative representation of the model's actual performance. Albeit accuracy is still deceptive without precision and recall.\n* * *\n## ROC Curves vs. Precision-Recall Curves\nROC curves are a common approach used to evaluate the performance of binary classification models. However, when dealing with imbalanced datasets, they can also provide over-optimistic and not entirely meaningful results.\n_**A brief overview of [ROC](**_[_**https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc#:~:text=An%20ROC%20curve%20(receiver%20operating,False%20Positive%20Rate**_](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc#:~:text=An%20ROC%20curve%20\\(receiver%20operating,False%20Positive%20Rate) _**) and Precision-Recall curves**_ _: we are essentially plotting the classification metrics against each other for different decision thresholds. We commonly measure the area under the curve (or AUC), to give us an indication of the models performance. Follow the links to learn more about ROC and_[ _Precision- Recall Curves_](https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/) _._\nTo illustrate how ROC curves can be over optimistic, I have built a classification model on a credit card fraud dataset taken from [Kaggle](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud). The dataset comprises 284,807 transactions, of which 492 are fraudulent.\n_Note: The data is free to use for commercial and non-commercial purposes without permission, as outlined in the_[ _Open Data Commons license_](https://opendatacommons.org/licenses/dbcl/1-0/) _attributed to the data._\nUpon examining the ROC curve, we might be led to believe the model performance is better than it actually is, since the area under this curve is 0.97. As we have previously seen, the false positive rate can be overly optimistic for imbalanced classification problems.\nA more robust approach would be to utilise the precision-recall curve. This provides a much more robust estimate of our model's performance. Here we can see the area under the precision-recall curve (AUC-PR) is much more conservative at 0.71.\nTaking a balanced version of the dataset where fraudulent and non-fraudulent transactions are 50:50, we can see that the AUC and AUC-PR are much closer together.\nThe notebook for generating these charts is available in my [GitHub repo](https://github.com/john-adeojo/Precision_Recall_Curves).\nThere are ways to uplift the performance of classification models on imbalanced datasets, I explore these in my article on synthetic data.\n> [**Can Synthetic Data Boost Machine Learning Performance?**](https://towardsdatascience.com/can-synthetic-data-boost-machine-learning-performance-6b4041e75dda)\n* * *\n## Conclusion\nUnderstanding classification model metrics goes beyond the mathematical formulae. You should also understand how each metric should be used, and their implications for both balanced and imbalanced datasets. As a rule of thumb, metrics that are calculated based on true negatives or false negatives may be over optimistic when they are applied to imbalanced datasets. I hope this visual tour has given you more of an intuition.\nI found this visual explanation handy for articulating the approach to my non-technical stakeholders. Feel free to share or borrow the approach.\n* * *\n_Follow me on_[ _LinkedIn_](https://www.linkedin.com/in/john-adeojo/)\n_Subscribe to medium to get more insights from me:_\n> [**Join Medium with my referral link - John Adeojo**](https://johnadeojo.medium.com/membership)\n_Should you be interested in integrating AI or data science into your business operations, we invite you to schedule a complimentary initial consultation with us:_\n> [**Book Online | Data-Centric Solutions**](https://www.data-centric-solutions.com/book-online)\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![](../images/body-ead068aa953e7b688a96.png)\nWritten By\n## [John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[See all from John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[Binary Classification](https://towardsdatascience.com/tag/binary-classification/)[Classification Models](https://towardsdatascience.com/tag/classification-models/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Statistics](https://towardsdatascience.com/tag/statistics/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F&title=How%20to%20Avoid%20Being%20Fooled%20by%20Model%20Accuracy)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F&title=How%20to%20Avoid%20Being%20Fooled%20by%20Model%20Accuracy)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F&text=How%20to%20Avoid%20Being%20Fooled%20by%20Model%20Accuracy)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](../images/001.jpg)](https://towardsdatascience.com/tackling-global-food-challenge-with-data-strategy-story-of-john-deere-a60664003495/)\n## [Tackling Global Food Challenge With Data Strategy - Story of John Deere](https://towardsdatascience.com/tackling-global-food-challenge-with-data-strategy-story-of-john-deere-a60664003495/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nJohn Deere provides agricultural machinery that essentially allows farmers to mechanically farm, grow crops at scale, and enable the...\n[Ekhtiar Syed](https://towardsdatascience.com/author/ekhtiar.syed/)\nFebruary 21, 20225 min read\n  * [![](../images/002.png)](https://towardsdatascience.com/eda-for-word-embeddings-224c524b5769/)\n## [EDA for Word Embeddings](https://towardsdatascience.com/eda-for-word-embeddings-224c524b5769/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nData scientists use EDA for everything. Why not word embeddings?\n[Harys Dalvi](https://towardsdatascience.com/author/crackalamoo/)\nJuly 12, 202415 min read\n  * [![](../images/003.png)](https://towardsdatascience.com/3-must-know-advanced-analytics-for-understanding-your-customers-f650f4ac7f57/)\n## [3 must-know advanced analytics for understanding your customers](https://towardsdatascience.com/3-must-know-advanced-analytics-for-understanding-your-customers-f650f4ac7f57/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nTop Use-cases for customer analytics and how to do them\n[Pranay Dave](https://towardsdatascience.com/author/pranay-dave9/)\nNovember 18, 20206 min read\n  * [![](../images/004.jpg)](https://towardsdatascience.com/the-best-interviews-ive-had-were-never-coding-challenges-95ec0cde765b/)\n## [The Best Interviews I've Had Were Never Coding Challenges](https://towardsdatascience.com/the-best-interviews-ive-had-were-never-coding-challenges-95ec0cde765b/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nDifferent Styles of Interviews that You Can Try Out\n[Rose Day](https://towardsdatascience.com/author/rjday/)\nFebruary 4, 20227 min read\n  * [![](../images/005.jpeg)](https://towardsdatascience.com/create-beautiful-art-from-your-personal-data-9dc0abfeeaf/)\n## [Create Beautiful Art from Your Personal Data](https://towardsdatascience.com/create-beautiful-art-from-your-personal-data-9dc0abfeeaf/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA Step by Step Tutorial to Create Beautiful Art using Data viz tools and Affinity Designer\n[Dr. Gregor Scheithauer](https://towardsdatascience.com/author/gscheithauer/)\nApril 9, 20217 min read\n  * [![](../images/006.png)](https://towardsdatascience.com/unconventional-sentiment-analysis-bert-vs-catboost-90645f2437a9/)\n## [Unconventional Sentiment Analysis: BERT vs. Catboost](https://towardsdatascience.com/unconventional-sentiment-analysis-bert-vs-catboost-90645f2437a9/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA penny saved is a penny earned\n[Taras Baranyuk](https://towardsdatascience.com/author/taras-baranyuk/)\nMarch 5, 20216 min read\n  * [![](../images/007.png)](https://towardsdatascience.com/pendragon-four-multi-agent-reinforcement-learning-with-fate-grand-order-80f6254754dd/)\n## [Pendragon Four: Multi-Agent Reinforcement Learning with Fate Grand Order](https://towardsdatascience.com/pendragon-four-multi-agent-reinforcement-learning-with-fate-grand-order-80f6254754dd/)\n[Deep Learning](https://towardsdatascience.com/category/artificial-intelligence/deep-learning/)\nMulti-agent reinforcement learning in a custom game environment to train 4 agents and have them play the mobile phone game Fate Grand Order\n[Michael Sugimura](https://towardsdatascience.com/author/michaelsugimura/)\nDecember 17, 201923 min read\n  * [![](../images/008.jpeg)](https://towardsdatascience.com/building-a-qa-research-chatbot-with-amazon-bedrock-and-langchain-677fbd19e3c1/)\n## [Building a QA Research Chatbot with Amazon Bedrock and LangChain](https://towardsdatascience.com/building-a-qa-research-chatbot-with-amazon-bedrock-and-langchain-677fbd19e3c1/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nOverview and Implementation with Python\n[Aashish Nair](https://towardsdatascience.com/author/aashishnair/)\nMarch 16, 20249 min read\n  * [![](../images/009.png)](https://towardsdatascience.com/lean-machine-learning-running-your-proof-of-concept-the-lean-startup-way-part-1-f9dbebb74d63/)\n## [Lean Machine Learning: Running your Proof of Concept the Lean Startup Way (part 1)](https://towardsdatascience.com/lean-machine-learning-running-your-proof-of-concept-the-lean-startup-way-part-1-f9dbebb74d63/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nHow to transform the way you build machine learning products\n[Leah Berg and Ray McLendon](https://towardsdatascience.com/author/datasciencerebalanced/)\nMay 31, 20229 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "metadata": {
    "title": "How to Avoid Being Fooled by Model Accuracy | Towards Data Science",
    "description": "A visual guide to binary classification model metrics and their proper usage",
    "keywords": null,
    "author": "John Adeojo",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "How to Avoid Being Fooled by Model Accuracy",
    "og:description": "A visual guide to binary classification model metrics and their proper usage",
    "og:url": "https://towardsdatascience.com/how-to-avoid-being-fooled-by-model-accuracy-e26307385fe1/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1N_KKkADV7dAOdcVvrv0-bQ.png",
    "og:image:width": "1024",
    "og:image:height": "1024",
    "og:image:type": "image/png",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "John Adeojo",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "8 min read",
    "article:published_time": "2023-07-07T06:04:48.000Z",
    "article:modified_time": "2023-07-07T06:04:48.000Z",
    "article:author": "https://towardsdatascience.com/author/johnadeojo/"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 202,
    "error": ""
  },
  "code_blocks": [],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:58:21Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:58:21Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:58:21Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:58:21Z"
    }
  ],
  "audio": [],
  "videos": [
    {
      "url": "https://www.youtube.com/c/TowardsDataScience",
      "kind": "channel_reference",
      "status": "reference_only"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/johnadeojo/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/can-synthetic-data-boost-machine-learning-performance-6b4041e75dda"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/binary-classification/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/classification-models/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/statistics/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tackling-global-food-challenge-with-data-strategy-story-of-john-deere-a60664003495/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/ekhtiar.syed/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/eda-for-word-embeddings-224c524b5769/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/crackalamoo/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/3-must-know-advanced-analytics-for-understanding-your-customers-f650f4ac7f57/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/ai-data-scientist"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/machine-learning"
    },
    {
      "type": "external",
      "href": "https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc"
    },
    {
      "type": "external",
      "href": "https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/"
    },
    {
      "type": "external",
      "href": "https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud"
    },
    {
      "type": "external",
      "href": "https://opendatacommons.org/licenses/dbcl/1-0/"
    },
    {
      "type": "external",
      "href": "https://github.com/john-adeojo/Precision_Recall_Curves"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/in/john-adeojo/"
    },
    {
      "type": "external",
      "href": "https://johnadeojo.medium.com/membership"
    },
    {
      "type": "external",
      "href": "https://www.data-centric-solutions.com/book-online"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=How+to+Avoid+Being+Fooled+by+Model+Accuracy&u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=How+to+Avoid+Being+Fooled+by+Model+Accuracy&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=How+to+Avoid+Being+Fooled+by+Model+Accuracy&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F"
    },
    {
      "type": "external",
      "href": "https://www.youtube.com/c/TowardsDataScience"
    },
    {
      "type": "external",
      "href": "https://www.threads.net/@towardsdatascience"
    },
    {
      "type": "external",
      "href": "https://bsky.app/profile/towardsdatascience.com"
    },
    {
      "type": "external",
      "href": "https://thenewstack.io"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-20T17:29:29Z",
  "source_url": "https://towardsdatascience.com/how-to-avoid-being-fooled-by-model-accuracy-e26307385fe1/",
  "delivery_status": "unverified",
  "discovery_status": "http_403",
  "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片",
  "repair_error": "",
  "actual_video_files": [
    {
      "local_path": "attachments/d7ab90784a04/videos/5aC4hruxBR4.f251.webm",
      "bytes": 7260088,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "492.661000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/5aC4hruxBR4.f298.mp4",
      "bytes": 21590071,
      "valid": true,
      "height": 720,
      "width": 1280,
      "duration": "492.650000",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/9PMjEOPUTV8.f251.webm",
      "bytes": 44871030,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "3343.781000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/9PMjEOPUTV8.f299.mp4",
      "bytes": 558574353,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "3343.750000",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/BaV698rqTVE.f251.webm",
      "bytes": 3215355,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "241.581000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/BaV698rqTVE.f399.mp4",
      "bytes": 6200916,
      "valid": true,
      "height": 1080,
      "width": 1888,
      "duration": "241.533333",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/DDG7AZo-t0M.f251.webm",
      "bytes": 8717799,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "693.981000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/DDG7AZo-t0M.f399.mp4",
      "bytes": 18251759,
      "valid": true,
      "height": 1080,
      "width": 1662,
      "duration": "693.966667",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/J76h5loFn1I.f137.mp4",
      "bytes": 325979873,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "2942.733333",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/J76h5loFn1I.f251.webm",
      "bytes": 39403360,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "2942.721000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/dYSw-SV_fsI.f137.mp4",
      "bytes": 510118997,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "3502.466667",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/dYSw-SV_fsI.f251.webm",
      "bytes": 50508020,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "3502.501000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/dw5Vff_fjoc.f251.webm",
      "bytes": 12502314,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "803.981000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/dw5Vff_fjoc.f399.mp4",
      "bytes": 33276990,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "803.966667",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/r41UzkiR8Hw.f251.webm",
      "bytes": 38694049,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "2899.101000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/r41UzkiR8Hw.f299.mp4",
      "bytes": 465524987,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "2899.100000",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/wE7MG20VT8g.f251.webm",
      "bytes": 34123989,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "2494.421000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/wE7MG20VT8g.f299.mp4",
      "bytes": 405392900,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "2494.400000",
      "status": "ok"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/zQ84HXl_kDc.f251.webm",
      "bytes": 43526388,
      "valid": false,
      "height": 0,
      "width": null,
      "duration": "3081.881000",
      "status": "invalid_or_wrong_resolution"
    },
    {
      "local_path": "attachments/d7ab90784a04/videos/zQ84HXl_kDc.f299.mp4",
      "bytes": 1084902539,
      "valid": true,
      "height": 1080,
      "width": 1920,
      "duration": "3081.866667",
      "status": "ok"
    }
  ],
  "expected_saved_body_images": 4,
  "image_discovery": "saved_markdown",
  "source_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. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. 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 Avoid Being Fooled by Model Accuracy\nA visual guide to binary classification model metrics and their proper usage\n[John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\nJuly 7, 20238 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1N_KKkADV7dAOdcVvrv0-bQ.png)\n## Background - Simple on the Surface\nThe metrics used for gauging performance of classification models are fairly straightforward, at least from a mathematical standpoint. Nevertheless, I have observed that many modellers and data scientists encounter difficulty articulating these metrics, and even apply them incorrectly. This is an easy mistake to make, as these metrics appear simple on the surface, yet their implications can be profound depending on the problem domain.\nThis article serves as a visual guide to explaining common classification model metrics. We will explore definitions and use examples to highlight where metrics are used inappropriately.\n### A Brief Note on Visualisation\nEach visualisation comprises of ninety subjects, representing anything we might wish to classify. Blue subjects denote negative samples, whilst red are positive samples. The purple box is the model which attempts to predict positive samples. Anything inside this box is what the model predicts as positive.\nWith that clarified, let's delve into the definitions.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n* * *\n## Precision & Recall\nFor many classification tasks there is a trade-off between precision and recall. It's frequently the case that optimising for recall incurs a cost to precision. But what do these terms actually mean? Let's begin with the mathematical definitions, and then move onto the visual representations.\n> Precision = TP/ (TP + FP)\n> Recall = TP/(TP + FN)\n_Where TP = Number of true positives, FP = Number of false positives, FN = Number of false negatives._\nLet's focus on the chart directly below in which there are four positive samples. Remember, the model's positive predictions are represented by the box on the chart. Observing the chart, we see the model correctly predicts all four positive samples— we can see this as all the positive samples sit within the box. We can calculate model recall from the chart by counting positive cases within the box (TP = 4) divided by the total number of positive cases (TP = 4 + FN = 0).\n_Note FN is 0 because there are no positive cases outside of the box._\nPrecision can be explained similarly. It is simply the number of positive cases in the box (TP=4) divided by the total number of cases in the box (TP = 4 + FP = 6). A straightforward calculation reveals the model's precision to be just 40%.\nYou can observe that a model can have a high recall but low precision, and vice versa. The chart below shows this, where recall is just 50%, while precision is 100%. See if you can internalise how to get to these numbers.\n_Here is a clue to help you, the number of false negatives is two since there are two positive samples outside of the box._\n* * *\n## False Positive Rates & True Negative Rates\nThe false positive rate (FPR) perhaps appears more intuitive, possibly due to its name. However, let's explore the concept in the same way we did for the other metrics. Mathematically, we express the FPR as follows:\n> FPR = FP/(FP + TN)\n_Here, TN represents the number of true negative subjects._\nExamining the first image again, the FPR can be determined by looking at the number of negative samples inside the box (FP=6) divided by the total number of negative samples (FP=6 + TN=80). For our first image, the false positive rate is just 7%, and for the second, it's 0%. Try figuring out why this is the case.\n_Remember, the subjects inside the box are those that the model_ _**predicts**_ _are positive. So by extension, the negative samples outside the box are those that the model has identified as negative._\nThe true negative rate (TNR) can be computed using the following formula:\n> TNR = TN/(TN + FP)\n_Notice that TNR is always one minus the false positive rate._\n* * *\n## Accuracy\nAccuracy is a term that is loosely thrown around in the context of model performance, but what does it actually mean? Let's start with the mathematical definition:\n> Accuracy = (TP + TN) / (TP + TN + FP + FN)\nUsing the same logic we previously applied, we can calculate the model's accuracy as 93% for the first image and 97% for the second (see if you can derive this for yourself). This might be raising red flags in your mind as to why accuracy can be a deceptive metric in some cases. We will explore this in greater detail next.\n* * *\n## Using Metrics Correctly\nWhy do we concern ourselves with these metrics? Because they equip us with ways to assess the performance of our models. Once we comprehend these metrics, we can even determine the commercial value associated with models. This is why it is important to have a good intuition around there appropriate (and inappropriate) use. To illustrate this, we will briefly investigate the two common scenarios in classification tasks, namely balanced and imbalanced datasets.\n* * *\n## Imbalanced Datasets\nThe diagrams depicted earlier are instances of imbalanced classification tasks. Put simply, imbalanced tasks have a low representation of positive subjects compared with negative subjects. Many commercial use cases for binary classification fall into this category like credit card fraud detection and customer churn prediction, spam filtering etc. Selecting the incorrect metrics for imbalanced classification can lead you to have over-optimistic beliefs about the performance of your model.\nThe primary issue with imbalanced classification is the potential for the number of true negative samples to be high, and false negatives to be low. To illustrate, let's consider another model and assess it on our imbalanced data. We can create an extreme scenario where the model simply predicts every subject as negative.\nLet's compute each of the metrics in this scenario.\n  * **Accuracy** : _(TP=0 + TN=86)/(TP=0 + TN=86 + FP=0 + FN=4) = 95%_\n  * **Precision** : _(TP=0) /(TP=0 + FP=0) = undefined_\n  * **Recall** : _(TP=0) / (TP=0 + FN=4) = 0%_\n  * **FPR** : _(FP=0) / (FP=0 + TN=86) = 0%_\n  * **TNR** : _(TN=86) / (TN=86 + FP=0) = 100%_\n\n\nThe issues with accuracy, FPR and TNR should start to become more apparent. When we are working with imbalanced datasets, we can produce a high-accuracy model that performs poorly upon deployment. In the previous example, the model has no capacity to detect positive subjects but still achieves an accuracy of 95%, A 0% FPR and a perfect TNR.\nNow, imagine deploying such a model to conduct medical diagnostics or detect fraud; it would quite evidently be useless and perhaps even dangerous. This extreme example illustrates the problem of using metrics such as accuracy, FPR, and TPR to assess the performance of models working on imbalanced data.\n* * *\n## Balanced Datasets\nFor balanced classification problems, the number of potential true negatives is significantly smaller than in the imbalanced case.\nIf we take our \"non-discriminatory\" model and apply it to the balanced case, we obtain the following results:\n  * **Accuracy** : _(TP=0 + TN=45) / (TP=0 + TN=45 + FP=0 + FN=45) = 50%_\n  * **Precision** : _(TP=0) / (TP=0 + FP=0) = undefined_\n  * **Recall** : _(TP=0) / (TP=0 + FN=45) = 0%_\n  * **FPR** : _(FP=0) / (FP=0 + TN=45) = 0%_\n  * **TNR** : _(TN=45) / (TN=45 + FP=0) = 100%_\n\n\nWhile all of the other metrics remain the same, the model's accuracy has declined to 50%, arguably a much more indicative representation of the model's actual performance. Albeit accuracy is still deceptive without precision and recall.\n* * *\n## ROC Curves vs. Precision-Recall Curves\nROC curves are a common approach used to evaluate the performance of binary classification models. However, when dealing with imbalanced datasets, they can also provide over-optimistic and not entirely meaningful results.\n_**A brief overview of [ROC](**_[_**https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc#:~:text=An%20ROC%20curve%20(receiver%20operating,False%20Positive%20Rate**_](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc#:~:text=An%20ROC%20curve%20\\(receiver%20operating,False%20Positive%20Rate) _**) and Precision-Recall curves**_ _: we are essentially plotting the classification metrics against each other for different decision thresholds. We commonly measure the area under the curve (or AUC), to give us an indication of the models performance. Follow the links to learn more about ROC and_[ _Precision- Recall Curves_](https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/) _._\nTo illustrate how ROC curves can be over optimistic, I have built a classification model on a credit card fraud dataset taken from [Kaggle](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud). The dataset comprises 284,807 transactions, of which 492 are fraudulent.\n_Note: The data is free to use for commercial and non-commercial purposes without permission, as outlined in the_[ _Open Data Commons license_](https://opendatacommons.org/licenses/dbcl/1-0/) _attributed to the data._\nUpon examining the ROC curve, we might be led to believe the model performance is better than it actually is, since the area under this curve is 0.97. As we have previously seen, the false positive rate can be overly optimistic for imbalanced classification problems.\nA more robust approach would be to utilise the precision-recall curve. This provides a much more robust estimate of our model's performance. Here we can see the area under the precision-recall curve (AUC-PR) is much more conservative at 0.71.\nTaking a balanced version of the dataset where fraudulent and non-fraudulent transactions are 50:50, we can see that the AUC and AUC-PR are much closer together.\nThe notebook for generating these charts is available in my [GitHub repo](https://github.com/john-adeojo/Precision_Recall_Curves).\nThere are ways to uplift the performance of classification models on imbalanced datasets, I explore these in my article on synthetic data.\n> [**Can Synthetic Data Boost Machine Learning Performance?**](https://towardsdatascience.com/can-synthetic-data-boost-machine-learning-performance-6b4041e75dda)\n* * *\n## Conclusion\nUnderstanding classification model metrics goes beyond the mathematical formulae. You should also understand how each metric should be used, and their implications for both balanced and imbalanced datasets. As a rule of thumb, metrics that are calculated based on true negatives or false negatives may be over optimistic when they are applied to imbalanced datasets. I hope this visual tour has given you more of an intuition.\nI found this visual explanation handy for articulating the approach to my non-technical stakeholders. Feel free to share or borrow the approach.\n* * *\n_Follow me on_[ _LinkedIn_](https://www.linkedin.com/in/john-adeojo/)\n_Subscribe to medium to get more insights from me:_\n> [**Join Medium with my referral link - John Adeojo**](https://johnadeojo.medium.com/membership)\n_Should you be interested in integrating AI or data science into your business operations, we invite you to schedule a complimentary initial consultation with us:_\n> [**Book Online | Data-Centric Solutions**](https://www.data-centric-solutions.com/book-online)\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/1787657091747_phu3q1.png)\nWritten By\n## [John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[See all from John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[Binary Classification](https://towardsdatascience.com/tag/binary-classification/)[Classification Models](https://towardsdatascience.com/tag/classification-models/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Statistics](https://towardsdatascience.com/tag/statistics/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F&title=How%20to%20Avoid%20Being%20Fooled%20by%20Model%20Accuracy)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F&title=How%20to%20Avoid%20Being%20Fooled%20by%20Model%20Accuracy)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-avoid-being-fooled-by-model-accuracy-e26307385fe1%2F&text=How%20to%20Avoid%20Being%20Fooled%20by%20Model%20Accuracy)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/02/0tSmIBFa3Hs3BER10-scaled.jpg)](https://towardsdatascience.com/tackling-global-food-challenge-with-data-strategy-story-of-john-deere-a60664003495/)\n## [Tackling Global Food Challenge With Data Strategy - Story of John Deere](https://towardsdatascience.com/tackling-global-food-challenge-with-data-strategy-story-of-john-deere-a60664003495/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nJohn Deere provides agricultural machinery that essentially allows farmers to mechanically farm, grow crops at scale, and enable the...\n[Ekhtiar Syed](https://towardsdatascience.com/author/ekhtiar.syed/)\nFebruary 21, 20225 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/07/1nIAqzEA1n-taUzRrHz5kBw.png)](https://towardsdatascience.com/eda-for-word-embeddings-224c524b5769/)\n## [EDA for Word Embeddings](https://towardsdatascience.com/eda-for-word-embeddings-224c524b5769/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nData scientists use EDA for everything. Why not word embeddings?\n[Harys Dalvi](https://towardsdatascience.com/author/crackalamoo/)\nJuly 12, 202415 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/11/1deedp8I7-x7GxJlL3LKsTw.png)](https://towardsdatascience.com/3-must-know-advanced-analytics-for-understanding-your-customers-f650f4ac7f57/)\n## [3 must-know advanced analytics for understanding your customers](https://towardsdatascience.com/3-must-know-advanced-analytics-for-understanding-your-customers-f650f4ac7f57/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nTop Use-cases for customer analytics and how to do them\n[Pranay Dave](https://towardsdatascience.com/author/pranay-dave9/)\nNovember 18, 20206 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/02/0cGeMLypdJxN6PWyv-scaled.jpg)](https://towardsdatascience.com/the-best-interviews-ive-had-were-never-coding-challenges-95ec0cde765b/)\n## [The Best Interviews I've Had Were Never Coding Challenges](https://towardsdatascience.com/the-best-interviews-ive-had-were-never-coding-challenges-95ec0cde765b/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nDifferent Styles of Interviews that You Can Try Out\n[Rose Day](https://towardsdatascience.com/author/rjday/)\nFebruary 4, 20227 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/04/1KoPRa4FKI5c5CIGmt00nZA-scaled.jpeg)](https://towardsdatascience.com/create-beautiful-art-from-your-personal-data-9dc0abfeeaf/)\n## [Create Beautiful Art from Your Personal Data](https://towardsdatascience.com/create-beautiful-art-from-your-personal-data-9dc0abfeeaf/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA Step by Step Tutorial to Create Beautiful Art using Data viz tools and Affinity Designer\n[Dr. Gregor Scheithauer](https://towardsdatascience.com/author/gscheithauer/)\nApril 9, 20217 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/03/1U2jCXCm6hdNbWSPcSwJSOw.png)](https://towardsdatascience.com/unconventional-sentiment-analysis-bert-vs-catboost-90645f2437a9/)\n## [Unconventional Sentiment Analysis: BERT vs. Catboost](https://towardsdatascience.com/unconventional-sentiment-analysis-bert-vs-catboost-90645f2437a9/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA penny saved is a penny earned\n[Taras Baranyuk](https://towardsdatascience.com/author/taras-baranyuk/)\nMarch 5, 20216 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2019/12/1igK-vCoAbj1nL7QIw25Hw.png)](https://towardsdatascience.com/pendragon-four-multi-agent-reinforcement-learning-with-fate-grand-order-80f6254754dd/)\n## [Pendragon Four: Multi-Agent Reinforcement Learning with Fate Grand Order](https://towardsdatascience.com/pendragon-four-multi-agent-reinforcement-learning-with-fate-grand-order-80f6254754dd/)\n[Deep Learning](https://towardsdatascience.com/category/artificial-intelligence/deep-learning/)\nMulti-agent reinforcement learning in a custom game environment to train 4 agents and have them play the mobile phone game Fate Grand Order\n[Michael Sugimura](https://towardsdatascience.com/author/michaelsugimura/)\nDecember 17, 201923 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/03/1ZEgcMNUT420iovmoCMLRrA.jpeg)](https://towardsdatascience.com/building-a-qa-research-chatbot-with-amazon-bedrock-and-langchain-677fbd19e3c1/)\n## [Building a QA Research Chatbot with Amazon Bedrock and LangChain](https://towardsdatascience.com/building-a-qa-research-chatbot-with-amazon-bedrock-and-langchain-677fbd19e3c1/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nOverview and Implementation with Python\n[Aashish Nair](https://towardsdatascience.com/author/aashishnair/)\nMarch 16, 20249 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/05/1e1FFP0WgbEWDnpPjPhVidQ.png)](https://towardsdatascience.com/lean-machine-learning-running-your-proof-of-concept-the-lean-startup-way-part-1-f9dbebb74d63/)\n## [Lean Machine Learning: Running your Proof of Concept the Lean Startup Way (part 1)](https://towardsdatascience.com/lean-machine-learning-running-your-proof-of-concept-the-lean-startup-way-part-1-f9dbebb74d63/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nHow to transform the way you build machine learning products\n[Leah Berg and Ray McLendon](https://towardsdatascience.com/author/datasciencerebalanced/)\nMay 31, 20229 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "body_base_path": "attachments/d7ab90784a04/pages/",
  "body_markdown_path": "attachments/d7ab90784a04/pages/archived-body.md",
  "local_html": "attachments/d7ab90784a04/pages/archived-body.html",
  "body_source": "prior_capture"
}