1.3 Terminology 1.3 Terminology To avoid confusion due to ambiguity, here are some definitions of terms used in this book: An Algorithm is a set of rules that a machine follo...
2.3 Scope of Interpretability 2.3.1 Algorithm Transparency 2.3.2 Global, Holistic Model Interpretability 2.3.3 Global Model Interpretability on a Modular Level 2.3.4 Local Inter...
6.3 Prototypes and Criticisms 6.3.1 Theory 6.3.2 Examples 6.3.3 Advantages 6.3.4 Disadvantages 6.3.5 Code and Alternatives 6.3 Prototypes and Criticisms A prototype is a ...
Access http:h2 Example About h2 Create Channel GET POST Change HTTP version URL Host header Common usages Debug HTTP messages HTTP errors Compress Request Body Decompr...
Access http:h2 Example About h2 Create Channel GET POST Change HTTP version URL Host header Common usages Debug HTTP messages HTTP errors Compress Request Body Decompr...
8.2 The Future of Interpretability 8.2 The Future of Interpretability Let us take a look at the possible future of machine learning interpretability. The focus will be on mode...
Chapter 9 Contribute to the Book Chapter 9 Contribute to the Book Thank you for reading my book about Interpretable Machine Learning. The book is under continuous development. ...
Chapter 8 A Look into the Crystal Ball Chapter 8 A Look into the Crystal Ball What is the future of interpretable machine learning? This chapter is a speculative mental exerc...
Chapter 5 Model-Agnostic Methods Chapter 5 Model-Agnostic Methods Separating the explanations from the machine learning model (= model-agnostic interpretation methods) has some...