{"product_id":"measurement-error-in-r-practical-techniques-using-simex-calibration-and-simulation-to-repair-imperfect-data-and-build-reliable-models-9798253831878","title":"Measurement Error in R: Practical Techniques Using Simex, Calibration, and Simulation to Repair Imperfect Data and Build Reliable Models","description":"\u003cp\u003e • Author(s): Walton Bryant\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Applied\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMeasurement Error in R: Fix Noisy Data, Reduce Bias, and Improve Machine Learning Models: Practical Techniques Using SIMEX, Calibration, and Simulation to Repair Imperfect Data and Build Reliable Models\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eMost models don't fail because of bad algorithms they fail because the data is wrong.\u003c\/p\u003e\u003cp\u003eIf you've ever built a model that looked correct but produced weak, unstable, or misleading results, the real issue may not be your method. It's the hidden measurement error inside your data.\u003c\/p\u003e\u003cp\u003eThis book shows you exactly how to detect it, measure it, and fix it using practical, real-world workflows in R.\u003c\/p\u003e\u003cp\u003eInstead of assuming your data is clean, you'll learn how to work with it as it actually exists: noisy, imperfect, and biased.\u003c\/p\u003e\u003cbr\u003eWHAT YOU'LL LEARN\u003cul\u003e\n\u003cli\u003eHow measurement error silently distorts regression and machine learning models\u003c\/li\u003e\n\u003cli\u003eStep-by-step methods to detect bias using diagnostics and simulation\u003c\/li\u003e\n\u003cli\u003ePractical implementation of \u003cb\u003eSIMEX, regression calibration, and errors-in-variables models\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003eHow to handle \u003cb\u003efeature noise and label noise in machine learning systems\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003eTechniques for correcting \u003cb\u003etime-series drift, sensor errors, and longitudinal data issues\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003eHow to use \u003cb\u003evalidation data and \"truth data\" to improve model accuracy\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003eA complete framework to build models that remain reliable under imperfect data\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eWHAT MAKES THIS BOOK DIFFERENT\u003cp\u003eThis is not a theory-heavy statistics book.\u003c\/p\u003e\u003cp\u003eIt is a \u003cb\u003epractical system for fixing broken models\u003c\/b\u003e caused by bad data.\u003c\/p\u003e\u003cp\u003eYou won't find abstract explanations or academic detours. Every chapter is built around: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cb\u003eReal workflows\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eR-based implementation\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eProblems you actually face in data science and analytics\u003c\/b\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eWHO THIS BOOK IS FOR\u003cul\u003e\n\u003cli\u003e\u003cb\u003eData scientists working with real-world datasets\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eAnalysts struggling with noisy or unreliable data\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eMachine learning practitioners dealing with unstable models\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eStatisticians who want practical error correction techniques\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eAnyone tired of models that \"work\" but can't be trusted\u003c\/b\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eWHY THIS MATTERS\u003cp\u003eIgnoring measurement error doesn't just reduce accuracy it leads to wrong decisions.\u003c\/p\u003e\u003cp\u003eFixing it gives you: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eStronger, more reliable models\u003c\/li\u003e\n\u003cli\u003eBetter interpretation of results\u003c\/li\u003e\n\u003cli\u003eConfidence in your analysis\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eWHAT YOU'LL BUILD\u003cp\u003eBy the end of this book, you will be able to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cb\u003eDiagnose when your model is wrong\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eQuantify how measurement error affects results\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eApply correction methods that actually work\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cb\u003eBuild data pipelines that don't fail silently\u003c\/b\u003e\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eIf your data isn't perfect and it never is, this book gives you the tools to make your models work anyway.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47775544803479,"sku":"9798253831878","price":1736.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798253831878.webp?v=1777991016","url":"https:\/\/atlanticbooks.com\/products\/measurement-error-in-r-practical-techniques-using-simex-calibration-and-simulation-to-repair-imperfect-data-and-build-reliable-models-9798253831878","provider":"Atlantic Books","version":"1.0","type":"link"}