Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Cover
Title page
Copyright and Credits
Dedications
Contributors
Table of Contents
Preface
Section 1 – Conceptual Exposure
Chapter 1: Foundational Concepts of Explainability Techniques
	Introduction to XAI
		Understanding the key terms
		Consequences of poor predictions
		Summarizing the need for model explainability
	Defining explanation methods and approaches
		Dimensions of explainability
		Addressing key questions of explainability
		Understanding different types of explanation methods
		Understanding the accuracy interpretability trade-off
	Evaluating the quality of explainability methods
		Criteria for good explainable ML systems
		Auxiliary criteria of XAI for ML systems
		Taxonomy of evaluation levels for explainable ML systems
	Summary
	References
Chapter 2: Model Explainability Methods
	Technical requirements
	Types of model explainability methods
	Knowledge extraction methods
		EDA
	Result visualization methods
		Using comparison analysis
		Using Surrogate Explainer methods
	Influence-based methods
		Feature importance
		Sensitivity analysis
		PDPs
		LRP
		Representation-based explanation
		VAMs
	Example-based methods
		CFEs in structured data
		CFEs in unstructured data
	Summary
	References
Chapter 3: Data-Centric Approaches
	Technical requirements
	Introduction to data-centric XAI
		Analyzing data volume
		Analyzing data consistency
		Analyzing data purity
	Thorough data analysis and profiling process
		The need for data analysis and profiling processes
		Data analysis as a precautionary step
		Building robust data profiles
	Monitoring and anticipating drifts
		Detecting drifts
		Selection of statistical measures
	Checking adversarial robustness
		Impact of adversarial attacks
		Methods to increase adversarial robustness
		Evaluating adversarial robustness
	Measuring data forecastability
		Estimating data forecastability
	Summary
	References
Section 2 – Practical Problem Solving
Chapter 4: LIME for Model Interpretability
	Technical requirements
	Intuitive understanding of LIME
		Learning interpretable data representations
		Maintaining a balance in the fidelity-interpretability trade-off
		Searching for local explorations
	What makes LIME a good model explainer?
	SP-LIME
	A practical example of using LIME for classification problems
	Potential pitfalls
	Summary
	References
Chapter 5: Practical Exposure to Using LIME in ML
	Technical requirements
	Using LIME on tabular data
		Setting up LIME
		Discussion about the dataset
		Discussions about the model
		Application of LIME
	Explaining image classifiers with LIME
		Setting up the required Python modules
		Using a pre-trained TensorFlow model as our black-box model
		Application of LIME Image Explainers
	Using LIME on text data
		Installing the required Python modules
		Discussions about the dataset used for training the model
		Discussions about the text classification model
		Applying LIME Text Explainers
	LIME for production-level systems
	Summary
	References
Chapter 6: Model Interpretability Using SHAP
	Technical requirements
	An intuitive understanding of the SHAP and Shapley values
		Introduction to SHAP and Shapley values
		What are Shapley values?
		Shapley values in ML
		The SHAP framework
	Model explainability approaches using SHAP
		Visualizations in SHAP
		Explainers in SHAP
	Using SHAP to explain regression models
		Setting up SHAP
		Inspecting the dataset
		Training the model
		Application of SHAP
	Advantages and limitations of SHAP
		Advantages
		Limitations
	Summary
	References
Chapter 7: Practical Exposure to Using SHAP in ML
	Technical requirements
	Applying TreeExplainers to tree ensemble models
		Installing the required Python modules
		Discussion about the dataset
		Training the model
		Application of TreeExplainer in SHAP
	Explaining deep learning models using DeepExplainer and GradientExplainer
		GradientExplainer
		Discussion on the dataset used for training the model
		Using a pre-trained CNN model for this example
		Application of GradientExplainer in SHAP
		Exploring DeepExplainers
		Application of DeepExplainer in SHAP
	Model-agnostic explainability using KernelExplainer
		Application of KernelExplainer in SHAP
	Exploring LinearExplainer in SHAP
		Application of LinearExplainer in SHAP
	Explaining transformers using SHAP
		Explaining transformer-based sentiment analysis models
		Explaining a multi-class prediction transformer model using SHAP
		Explaining zero-shot learning models using SHAP
	Summary
	References
Chapter 8: Human-Friendly Explanations with TCAV
	Technical requirements
	Understanding TCAV intuitively
		What is TCAV?
		Explaining with abstract concepts
		Goals of TCAV
		Approach of TCAV
	Exploring the practical applications of TCAV
		Getting started
		About the data
		Discussions about the deep learning model used
		Model explainability using TCAV
	Advantages and limitations
		Advantages
		Limitations
	Potential applications of concept-based explanations
	Summary
	References
Chapter 9: Other Popular XAI Frameworks
	Technical requirements
	DALEX
		Setting up DALEX for model explainability
		Discussions about the dataset
		Training the model
		Model explainability using DALEX
		Model-level explanations
		Prediction-level explanations
		Evaluating model fairness
		Interactive dashboards using ARENA
	Explainerdashboard
		Setting up Explainerdashboard
		Model explainability with Explainerdashboard
	InterpretML
		Supported explanation methods
		Setting up InterpretML
		Discussions about the dataset
		Training the model
		Explainability with InterpretML
	ALIBI
		Setting up ALIBI
		Discussion about the dataset
		Training the model
		Model explainability with ALIBI
	DiCE
		CFE methods supported in DiCE
		Model explainability with DiCE
	ELI5
		Setting up ELI5
		Model explainability using ELI5
	H2O AutoML explainers
		Explainability with H2O explainers
	Quick comparison guide
	Summary
	References
Section 3 –  Taking XAI to the Next Level
Chapter 10: XAI Industry Best Practices
	Open challenges of XAI
	Guidelines for designing explainable ML systems
	Adopting a data-first approach for explainability
	Emphasizing IML for explainability
	Emphasizing prescriptive insights for explainability
	Summary
	References
Chapter 11: End User-Centered Artificial Intelligence
	User-centered XAI/ML systems
		Different aspects of end user-centric XAI
	Rapid XAI prototyping using EUCA
	Efforts toward increasing user acceptance of AI/ML systems using XAI
	Providing a delightful UX
	Summary
	References
Index
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