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: Paperback / softback
Length: 310 pages
Publication date: 26 September 2022
Publisher: Taylor & Francis Ltd

Exploratory Model Analysis is a comprehensive set of methods and tools aimed at enhancing the development and evaluation of predictive models. In the current landscape of predictive modeling, the primary obstacle lies not in the scarcity of data, computational power, or inadequate algorithms. Rather, the challenge revolves around the lack of effective tools for exploring, explaining, and examining these models. This book offers a valuable contribution by presenting a collection of model agnostic approaches that can be applied to various black-box models, encompassing classification and regression problems.

The importance of model exploration lies in the ability to extract valuable insights and relationships learned by the model. By understanding the underlying patterns and factors that influence the model's decisions, researchers can gain a deeper understanding of the model's behavior and improve its accuracy and reliability. This exploration process involves techniques such as feature importance analysis, data visualization, and model diagnostics, which help identify patterns, biases, and limitations of the model.

Model explanation plays a crucial role in facilitating the interpretation and understanding of the model's decisions. It involves identifying the key factors that influence the model's outputs and understanding the underlying logic and assumptions that drive the model. This explanation process helps stakeholders to assess the trustworthiness and reliability of the model and make informed decisions based on its predictions.

Model examination, on the other hand, involves identifying and evaluating the weaknesses and limitations of the model. This involves assessing the model's performance on unseen data, identifying areas where the model may overfit or underfit, and evaluating the generalizability and transferability of the model to different domains and scenarios. By examining the model, researchers can identify areas for improvement and refine the model to enhance its performance and accuracy.

The book presents a diverse range of model agnostic methods that can be applied to various black-box models. These methods include feature importance analysis, decision trees, and Shapley values, among others. Each method is accompanied by real-world applications and case studies, demonstrating their practical utility and effectiveness in addressing classification and regression problems.

In conclusion, Exploratory Model Analysis is a vital tool for building better predictive models and monitoring their behavior in a changing environment. By addressing the lack of tools for model exploration, explanation, and examination, this book provides researchers and practitioners with the necessary tools to enhance the development and evaluation of predictive models. The comprehensive collection of methods and real-world applications presented in this book makes it an invaluable resource for anyone interested in predictive modeling and data analysis.

Weight: 538g
Dimension: 155 x 235 x 20 (mm)
ISBN-13: 9780367693923