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Conceptual variable design for score...

Zapiain, Saul Rodrigo Alvarez.

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Conceptual variable design for scorecards = a standardized methodology for the model-building process /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Conceptual variable design for scorecards/ by Saul Rodrigo Alvarez Zapiain.
其他題名:	a standardized methodology for the model-building process /
作者:	Zapiain, Saul Rodrigo Alvarez.
出版者:	Berkeley, CA :Apress : : 2025.,
面頁冊數:	xxxv, 720 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Conceptual Representations -- Chapter 2: Conceptual Modelling -- Chapter 3: Balance Equation -- Chapter 4: Ratios -- Chapter 5: Time and Behavioral Patterns -- Chapter 6: Additional Variables -- Chapter 7: Things to Know About ABTs -- Chapter 8 The Building Plan and Variable Management -- Chapter 9: Target Population -- Chapter 10: The ABT Building Process -- Chapter 11: A Brief Introduction to the use of SAS® Enterprise MinerTM -- Chapter 12: Partitioning -- Chapter 13: Univariable Analysis -- Chapter 14: Collinearity Analysis -- Chapter 15: Weight of Evidence -- Chapter 16: Multivariable Selection Methods -- Chapter 17: Experimental Design and Hyperoptimization -- Chapter 18: The Main-Effects Model -- Chapter 19: The Scoring Process -- Chapter 20: Closing Thoughts.
Contained By:	Springer Nature eBook
標題:	Decision making - Mathematical models. -
電子資源:	https://doi.org/10.1007/979-8-8688-1421-1
ISBN:	9798868814211

Conceptual variable design for scorecards = a standardized methodology for the model-building process /
Zapiain, Saul Rodrigo Alvarez.

Conceptual variable design for scorecardsa standardized methodology for the model-building process /[electronic resource] :by Saul Rodrigo Alvarez Zapiain. - Berkeley, CA :Apress :2025. - xxxv, 720 p. :ill., digital ;24 cm.

Chapter 1: Conceptual Representations -- Chapter 2: Conceptual Modelling -- Chapter 3: Balance Equation -- Chapter 4: Ratios -- Chapter 5: Time and Behavioral Patterns -- Chapter 6: Additional Variables -- Chapter 7: Things to Know About ABTs -- Chapter 8 The Building Plan and Variable Management -- Chapter 9: Target Population -- Chapter 10: The ABT Building Process -- Chapter 11: A Brief Introduction to the use of SAS® Enterprise MinerTM -- Chapter 12: Partitioning -- Chapter 13: Univariable Analysis -- Chapter 14: Collinearity Analysis -- Chapter 15: Weight of Evidence -- Chapter 16: Multivariable Selection Methods -- Chapter 17: Experimental Design and Hyperoptimization -- Chapter 18: The Main-Effects Model -- Chapter 19: The Scoring Process -- Chapter 20: Closing Thoughts.

Embark on a journey through the intricate landscape of predictive modeling, where the fusion of conceptual clarity and robust statistical techniques creates powerful tools for decision-making. This book distills years of experience into a standardized methodology that empowers professionals across industries-from banking to telecommunications-to construct scorecards that predict outcomes with precision and confidence. In a world driven by data, the ability to transform complex information into actionable insights is paramount. This is your essential guide to mastering the art and science of model building. With practical examples, real-world case studies, and step-by-step guidance, this book is not just a resource-it's a roadmap to success in the rapidly evolving field of analytics. By focusing on reducing operational risk, you'll be equipped to make informed decisions that safeguard your organization's future. Whether you're a seasoned data scientist or just starting your journey, Conceptual Variable Design for Scorecards will provide you with the knowledge and skills to thrive in an era where data-driven decisions are the key to competitive advantage. Join the ranks of forward-thinking professionals who are redefining the future of risk management and predictive analytics. Your journey begins here. You will: Harness the power of conceptualization to create models that solve real-world problems. Design meaningful variables that reflect the behaviors of your target population. Expand variables with temporal patterns to capture trends and dynamic changes. Master data integration to streamline preparation and avoid common pitfalls. Implement a unified workflow to simplify and accelerate the modeling process. Explore a larger number of variables in your multivariable models by harnessing the use of experimental design and hyperoptimization.

ISBN: 9798868814211

Standard No.: 10.1007/979-8-8688-1421-1doiSubjects--Topical Terms:

565918
Decision making
--Mathematical models.

LC Class. No.: HD30.23 / .Z37 2025

Dewey Class. No.: 003.56

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520 $a Embark on a journey through the intricate landscape of predictive modeling, where the fusion of conceptual clarity and robust statistical techniques creates powerful tools for decision-making. This book distills years of experience into a standardized methodology that empowers professionals across industries-from banking to telecommunications-to construct scorecards that predict outcomes with precision and confidence. In a world driven by data, the ability to transform complex information into actionable insights is paramount. This is your essential guide to mastering the art and science of model building. With practical examples, real-world case studies, and step-by-step guidance, this book is not just a resource-it's a roadmap to success in the rapidly evolving field of analytics. By focusing on reducing operational risk, you'll be equipped to make informed decisions that safeguard your organization's future. Whether you're a seasoned data scientist or just starting your journey, Conceptual Variable Design for Scorecards will provide you with the knowledge and skills to thrive in an era where data-driven decisions are the key to competitive advantage. Join the ranks of forward-thinking professionals who are redefining the future of risk management and predictive analytics. Your journey begins here. You will: Harness the power of conceptualization to create models that solve real-world problems. Design meaningful variables that reflect the behaviors of your target population. Expand variables with temporal patterns to capture trends and dynamic changes. Master data integration to streamline preparation and avoid common pitfalls. Implement a unified workflow to simplify and accelerate the modeling process. Explore a larger number of variables in your multivariable models by harnessing the use of experimental design and hyperoptimization.
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