Surrogate Data Models
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Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models
Author: Jorge Chan-Lau
language: en
Publisher: International Monetary Fund
Release Date: 2023-02-24
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
Data Modeling with Snowflake
Author: Serge Gershkovich
language: en
Publisher: Packt Publishing Ltd
Release Date: 2023-05-31
Discover how Snowflake's unique objects and features can be used to leverage universal modeling techniques through real-world examples and SQL recipes Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn core modeling techniques tied to practical examples using native Snowflake architecture Adopt a universal modeling language to communicate business value to functional teams Go beyond physical modeling with SQL recipes to transform and shape your Snowflake data Book DescriptionThe Snowflake Data Cloud is one of the fastest-growing platforms for data warehousing and application workloads. Snowflake's scalable, cloud-native architecture and expansive set of features and objects enables you to deliver data solutions quicker than ever before. Yet, we must ensure that these solutions are developed using recommended design patterns and accompanied by documentation that’s easily accessible to everyone in the organization. This book will help you get familiar with simple and practical data modeling frameworks that accelerate agile design and evolve with the project from concept to code. These universal principles have helped guide database design for decades, and this book pairs them with unique Snowflake-native objects and examples like never before – giving you a two-for-one crash course in theory as well as direct application. By the end of this Snowflake book, you’ll have learned how to leverage Snowflake’s innovative features, such as time travel, zero-copy cloning, and change-data-capture, to create cost-effective, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples.What you will learn Discover the time-saving benefits and applications of data modeling Learn about Snowflake's cloud-native architecture and its features Understand and apply modeling techniques using Snowflake objects Universal modeling concepts and language through Snowflake objects Get comfortable reading and transforming semistructured data Learn directly with pre-built recipes and examples Learn to apply modeling frameworks from Star to Data Vault Who this book is for This book is for developers working with SQL who are looking to build a strong foundation in modeling best practices and gain an understanding of where they can be effectively applied to save time and effort. Whether you’re an ace in SQL logic or starting out in database design, this book will equip you with the practical foundations of data modeling to guide you on your data journey with Snowflake. Developers who’ve recently discovered Snowflake will be able to uncover its core features and learn to incorporate them into universal modeling frameworks.
Multi-objective Design Of Antennas Using Surrogate Models
This book addresses computationally-efficient multi-objective optimization of antenna structures using variable-fidelity electromagnetic simulations, surrogate modeling techniques, and design space reduction methods. Based on contemporary research, it formulates multi-objective design tasks, highlights related challenges in the context of antenna design, and discusses solution approaches. Specific focus is on providing methodologies for handling computationally expensive simulation models of antenna structures in the sense of their multi-objective optimization. Also given is a summary of recent developments in antenna design optimization using variable-fidelity simulation models. Numerous examples of real-world antenna design problems are provided along with discussions and recommendations for the readers interested in applying the considered methods in their design work.Written with researchers and students in mind, topics covered can also be applied across a broad spectrum of aeronautical, mechanical, electrical, biomedical and civil engineering. It is of particular interest to those dealing with optimization, computationally expensive design tasks and simulation-driven design.