Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models (Chapman & Hall/CRC Data Science Series)

Product Description 
Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems. 
 Review 
"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices. 
The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code. 
The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this. 
The rationale for the book is well-described. 
The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels." (Jeff Webb, University of Utah)\n"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful." (Riccardo De Bin, University of Oslo)\n"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other." (Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences)\n"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular." (Ziv Shkedy, Data Science Institute, Hasselt University, Belgium)\nAbout the Author 
Przemyslaw Biecek is a professor in human-oriented machine learning at the Warsaw University of Technology and Principal Data Scientist in Samsung R&D Institute Poland. His main research project is DrWhy.AI - tools and methods for exploration, explanation, visualisation, and debugging of predictive models. 
Tomasz Burzykowski is professor of biostatistics at Hasselt University and Vice-President for Research at International Drug Development Institute (IDDI). He has published extensively on applications of statistics in medicine and biology.

Business & Money

Burzykowski, Tomasz

Biecek, Przemysław

Economics

Chapman and Hall/CRC

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

This book is a unique entanglement of theory, examples and processes relevant to Responsible Machine Learning. You will find intuitions and examples for Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI). Descriptions are supplemented by code snippets with examples for R with the use of randomForest, mlr3 and DALEX packages. Finally, the process is shown through a comic book describing the adventures of two characters, Beta and Bit. The interaction of these two shows the decisions that analysts often face, whether to try a different model, try another technique for exploration or look for other data -- questions like how to compare models or validate them. All examples are fully reproducible so that one can replay all adventures on a local desktop. Model development is a responsible and challenging task but also an exciting adventure. Sometimes textbooks focus only on the technical side, losing all the fun. Here we are going to have it all.

The Hitchhiker's Guide to Responsible Machine Learning: Interpretable and eXplainable Artificial Intelligence with examples in R

Kozak, Anna

Education & Reference

Scientific Foundation SmarterPoland.pl

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