In the real world of data science, perfect datasets are a myth—most are skewed, with rare events like fraud, disease outbreaks, or customer churn hiding in a sea of normalcy. Machine Learning with Imbalanced Data: Proven Techniques to Build Accurate Models from Skewed Datasets equips you with battle-tested strategies to conquer this challenge and create models that truly perform. From foundational concepts like evaluating class distributions to advanced methods such as SMOTE variants, ensemble boosting, and cost-sensitive algorithms, this hands-on guide takes you step-by-step through Python-based projects using libraries like scikit-learn and imbalanced-learn.
Explore practical applications in fraud detection, medical diagnosis, and marketing analytics, with detailed case studies, code snippets, and exercises that build from beginner-friendly setups to production-ready pipelines. Learn to avoid common pitfalls like misleading accuracy metrics, data leakage, and overfitting, while mastering tools to deploy robust models via MLOps. Whether you're a data enthusiast starting out or a seasoned practitioner refining your skills, this book transforms imbalanced data from a hurdle into a superpower, ensuring your predictions catch the critical outliers that drive real impact.
1148503739
Explore practical applications in fraud detection, medical diagnosis, and marketing analytics, with detailed case studies, code snippets, and exercises that build from beginner-friendly setups to production-ready pipelines. Learn to avoid common pitfalls like misleading accuracy metrics, data leakage, and overfitting, while mastering tools to deploy robust models via MLOps. Whether you're a data enthusiast starting out or a seasoned practitioner refining your skills, this book transforms imbalanced data from a hurdle into a superpower, ensuring your predictions catch the critical outliers that drive real impact.
MACHINE LEARNING WITH IMBALANCED DATA: Proven Techniques to Build Accurate Models from Skewed Datasets
In the real world of data science, perfect datasets are a myth—most are skewed, with rare events like fraud, disease outbreaks, or customer churn hiding in a sea of normalcy. Machine Learning with Imbalanced Data: Proven Techniques to Build Accurate Models from Skewed Datasets equips you with battle-tested strategies to conquer this challenge and create models that truly perform. From foundational concepts like evaluating class distributions to advanced methods such as SMOTE variants, ensemble boosting, and cost-sensitive algorithms, this hands-on guide takes you step-by-step through Python-based projects using libraries like scikit-learn and imbalanced-learn.
Explore practical applications in fraud detection, medical diagnosis, and marketing analytics, with detailed case studies, code snippets, and exercises that build from beginner-friendly setups to production-ready pipelines. Learn to avoid common pitfalls like misleading accuracy metrics, data leakage, and overfitting, while mastering tools to deploy robust models via MLOps. Whether you're a data enthusiast starting out or a seasoned practitioner refining your skills, this book transforms imbalanced data from a hurdle into a superpower, ensuring your predictions catch the critical outliers that drive real impact.
Explore practical applications in fraud detection, medical diagnosis, and marketing analytics, with detailed case studies, code snippets, and exercises that build from beginner-friendly setups to production-ready pipelines. Learn to avoid common pitfalls like misleading accuracy metrics, data leakage, and overfitting, while mastering tools to deploy robust models via MLOps. Whether you're a data enthusiast starting out or a seasoned practitioner refining your skills, this book transforms imbalanced data from a hurdle into a superpower, ensuring your predictions catch the critical outliers that drive real impact.
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MACHINE LEARNING WITH IMBALANCED DATA: Proven Techniques to Build Accurate Models from Skewed Datasets
MACHINE LEARNING WITH IMBALANCED DATA: Proven Techniques to Build Accurate Models from Skewed Datasets
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Product Details
| BN ID: | 2940184628240 |
|---|---|
| Publisher: | Kirstin Hahn |
| Publication date: | 10/11/2025 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| File size: | 305 KB |
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