Interpreting Machine Learning Models : Learn Model Interpretability and Explainability Methods By Anirban Nandi, Aditya Kumar Pal

By:

Nandi, Anirban & Pal, Aditya Kumar

Material type: Text

TextPublication details: Apress, c2022.Edition: 1Description: i-xxiii+343PISBN:

9781484284094

Subject(s):

Electronic books Machine learning Mathematical models

DDC classification:

1 006.31 NAN-I

Online resources:

Online Resource

Contents:

Chapter 1: The Evolution of Machine Learning Chapter 2: Introduction to Model interpretability Chapter 3: Machine Learning Interpretability Taxonomy Chapter 4: Common Properties of Explanations Generated by Interpretability Methods Chapter 5: Human Factors in Model Interpretability Chapter 6: Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches Chapter 7: Interpretable ML and Explainable ML Differences Chapter 8: Framework of Model Explanations Chapter 9: Feature Importance methods Details and usage examples Chapter 10: Detailing rule-based methods Chapter 11: Detailing Counterfactual Methods Chapter 12: Detailing Image interpretability methods Chapter 13: Explaining text classification models Chapter 14: Role of Data in Interpretability Chapter 15: The 8 pitfalls of explainability methods

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Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics	Dept. of Computational Biology and Bioinformatics	006.31 NAN-I (Browse shelf(Opens below))	Available		DCB4165

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Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms.

Chapter 1: The Evolution of Machine Learning
Chapter 2: Introduction to Model interpretability
Chapter 3: Machine Learning Interpretability Taxonomy
Chapter 4: Common Properties of Explanations Generated by Interpretability Methods
Chapter 5: Human Factors in Model Interpretability
Chapter 6: Explainability Facts: A Framework for Systematic Assessment of Explainable Approaches
Chapter 7: Interpretable ML and Explainable ML Differences
Chapter 8: Framework of Model Explanations
Chapter 9: Feature Importance methods
Details and usage examples
Chapter 10: Detailing rule-based methods
Chapter 11: Detailing Counterfactual Methods
Chapter 12: Detailing Image interpretability methods
Chapter 13: Explaining text classification models
Chapter 14: Role of Data in Interpretability
Chapter 15: The 8 pitfalls of explainability methods

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