ISBN: 9798253831878 - measurement error in r: practical techniques using simex, calibration, and simulation to repair imperfect data and build reliable models (7 risultati)

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: new. Paperback. Measurement 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 ModelsMost models don't fail because of bad algorithms they fail because the data is wrong.If 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.This book shows you exactly how to detect it, measure it, and fix it using practical, real-world workflows in R.Instead of assuming your data is clean, you'll learn how to work with it as it actually exists: noisy, imperfect, and biased.WHAT YOU'LL LEARNHow measurement error silently distorts regression and machine learning modelsStep-by-step methods to detect bias using diagnostics and simulationPractical implementation of SIMEX, regression calibration, and errors-in-variables modelsHow to handle feature noise and label noise in machine learning systemsTechniques for correcting time-series drift, sensor errors, and longitudinal data issuesHow to use validation data and "truth data" to improve model accuracyA complete framework to build models that remain reliable under imperfect dataWHAT MAKES THIS BOOK DIFFERENTThis is not a theory-heavy statistics book.It is a practical system for fixing broken models caused by bad data.You won't find abstract explanations or academic detours. Every chapter is built around: Real workflowsR-based implementationProblems you actually face in data science and analyticsWHO THIS BOOK IS FORData scientists working with real-world datasetsAnalysts struggling with noisy or unreliable dataMachine learning practitioners dealing with unstable modelsStatisticians who want practical error correction techniquesAnyone tired of models that "work" but can't be trustedWHY THIS MATTERSIgnoring measurement error doesn't just reduce accuracy it leads to wrong decisions.Fixing it gives you: Stronger, more reliable modelsBetter interpretation of resultsConfidence in your analysisWHAT YOU'LL BUILDBy the end of this book, you will be able to: Diagnose when your model is wrongQuantify how measurement error affects resultsApply correction methods that actually workBuild data pipelines that don't fail silently If your data isn't perfect and it never is, this book gives you the tools to make your models work anyway. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Measurement 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 ModelsMost models don't fail because of bad algorithms they fail because the data is wrong.If 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.This book shows you exactly how to detect it, measure it, and fix it using practical, real-world workflows in R.Instead of assuming your data is clean, you'll learn how to work with it as it actually exists: noisy, imperfect, and biased.WHAT YOU'LL LEARNHow measurement error silently distorts regression and machine learning modelsStep-by-step methods to detect bias using diagnostics and simulationPractical implementation of SIMEX, regression calibration, and errors-in-variables modelsHow to handle feature noise and label noise in machine learning systemsTechniques for correcting time-series drift, sensor errors, and longitudinal data issuesHow to use validation data and "truth data" to improve model accuracyA complete framework to build models that remain reliable under imperfect dataWHAT MAKES THIS BOOK DIFFERENTThis is not a theory-heavy statistics book.It is a practical system for fixing broken models caused by bad data.You won't find abstract explanations or academic detours. Every chapter is built around: Real workflowsR-based implementationProblems you actually face in data science and analyticsWHO THIS BOOK IS FORData scientists working with real-world datasetsAnalysts struggling with noisy or unreliable dataMachine learning practitioners dealing with unstable modelsStatisticians who want practical error correction techniquesAnyone tired of models that "work" but can't be trustedWHY THIS MATTERSIgnoring measurement error doesn't just reduce accuracy it leads to wrong decisions.Fixing it gives you: Stronger, more reliable modelsBetter interpretation of resultsConfidence in your analysisWHAT YOU'LL BUILDBy the end of this book, you will be able to: Diagnose when your model is wrongQuantify how measurement error affects resultsApply correction methods that actually workBuild data pipelines that don't fail silently If your data isn't perfect and it never is, this book gives you the tools to make your models work anyway. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…