Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models

Exploratory Model Analysis explores, explains, and examines predictive models to build better ones and monitor their behavior in a changing environment. It presents a collection of model agnostic methods for any black-box model and real-world applications.

Format: Hardback
Length: 310 pages
Publication date: 18 March 2021
Publisher: Taylor & Francis Ltd

Exploratory Model Analysis is a comprehensive set of approaches and tools aimed at enhancing the accuracy and reliability of predictive models. In today's landscape, the primary obstacle to effective predictive modeling lies not in the scarcity of data, computational power, or inadequate algorithms. Rather, it is the lack of specialized tools for exploring, explaining, and examining these models. This book offers a valuable resource by presenting a collection of model agnostic methods that can be applied to a wide range of black-box models, encompassing classification and regression problems.

The first chapter of the book delves into the concept of exploratory model analysis, providing a comprehensive overview of its objectives and significance. It highlights the importance of understanding the underlying relationships and patterns within data, which can be achieved through the exploration of predictive models. The chapter also emphasizes the need for model explanation, which helps in deciphering the key factors that influence model decisions and their implications.

The subsequent chapters delve into specific methods and techniques for exploring, explaining, and examining predictive models. These methods include feature importance analysis, which identifies the most influential features in a model's predictions; partial dependence plots, which visualize the relationships between features and predictions; and Shapley values, which provide a measure of the contribution of each feature to the model's overall prediction.

Furthermore, the book discusses real-world applications of exploratory model analysis, showcasing how these methods can be applied to various industries and problem domains. It provides examples of successful applications in areas such as healthcare, finance, marketing, and natural language processing. These case studies demonstrate the practical benefits of using exploratory model analysis in improving model performance, identifying biases, and gaining insights into the underlying mechanisms of predictive models.

In conclusion, Exploratory Model Analysis is a valuable resource for researchers, practitioners, and data scientists seeking to improve the performance and interpretability of predictive models. By providing a comprehensive set of methods and techniques, the book enables users to gain a deeper understanding of the underlying mechanisms of these models, identify biases, and make informed decisions based on their findings.

Weight: 690g
Dimension: 159 x 243 x 22 (mm)
ISBN-13: 9780367135591