دانلود کتاب Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples

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Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Section 1: Introduction to Machine Learning Interpretation
Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter?
Technical requirements
What is machine learning interpretation?
Understanding a simple weight prediction model
Understanding the difference between interpretability and explainability
What is interpretability?
What is explainability?
A business case for interpretability
Better decisions
More trusted brands
More ethical
More profitable
Summary
Image sources
Further reading
Chapter 2: Key Concepts of Interpretability
Technical requirements
The mission
Details about CVD
The approach
Preparations
Loading the libraries
Understanding and preparing the data
Learning about interpretation method types and scopes
Model interpretability method types
Model interpretability scopes
Interpreting individual predictions with logistic regression
Appreciating what hinders machine learning interpretability
Non-linearity
Interactivity
Non-monotonicity
Mission accomplished
Summary
Further reading
Chapter 3: Interpretation Challenges
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Reviewing traditional model interpretation methods
Predicting minutes delayed with various regression methods
Classifying flights as delayed or not delayed with various classification methods
Visualizing delayed flights with dimensionality reduction methods
Understanding limitations of traditional model interpretation methods
Studying intrinsically interpretable (white-box) models
Generalized Linear Models (GLMs)
Decision trees
RuleFit
Nearest neighbors
Naïve Bayes
Recognizing the trade-off between performance and interpretability
Special model properties
Assessing performance
Discovering newer interpretable (glass-box) models
Explainable Boosting Machine (EBM)
Skoped Rules
Mission accomplished
Summary
Dataset sources
Further reading
Section 2: Mastering Interpretation Methods
Chapter 4: Fundamentals of Feature Importance and Impact
Technical requirements
The mission
Personality and birth order
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Measuring the impact of a feature on the outcome
Feature importance for tree-based models
Feature importance for Logistic Regression
Feature importance for LDA
Feature importance for the Multi-layer Perceptron
Practicing PFI
Disadvantages of PFI
Interpreting PDPs
Interaction PDPs
Disadvantages of PDP
Explaining ICE plots
Disadvantages of ICE
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 5: Global Model-Agnostic Interpretation Methods
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Learning about Shapley values
Interpreting SHAP summary and dependence plots
Generating SHAP summary plots
Understanding interactions
SHAP dependence plots
SHAP force plots
Accumulated Local Effects (ALE) plots
Global surrogates
Mission accomplished
Summary
Further reading
Chapter 6: Local Model-Agnostic Interpretation Methods
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Leveraging SHAP\'s KernelExplainer for local interpretations with SHAP values
Employing LIME
Using LIME for NLP
Trying SHAP for NLP
Comparing SHAP with LIME
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 7: Anchor and Counterfactual Explanations
Technical requirements
The mission
Unfair bias in recidivisim risk assessments
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Understanding anchor explanations
Preparations for anchor and counterfactual explanations with alibi
Local interpretations for anchor explanations
Exploring counterfactual explanations
Counterfactual explanations guided by prototypes
Counterfactual instances and much more with the What-If Tool (WIT)
Comparing with CEM
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 8: Visualizing Convolutional Neural Networks
Technical requirements
The mission
The approach
Preparations
Loading the libraries
Understanding and preparing the data
Assessing the CNN classifier with traditional interpretation methods
Visualizing the learning process with activation-based methods
Intermediate activations
Activation maximization
Evaluating misclassifications with gradient-based attribution methods
Saliency maps
Grad-CAM
Integrated gradients
Tying it all together
Understanding classifications with perturbation-based attribution methods
Occlusion sensitivity
LIME\'s ImageExplainer
CEM
Tying it all together
Bonus method: SHAP\'s DeepExplainer
Mission accomplished
Summary
Dataset and image sources
Further reading
Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
Technical requirements
The mission
The approach
The preparation
Loading the libraries
Understanding and preparing the data
Assessing time series models with traditional interpretation methods
Generating LSTM attributions with integrated gradients
Computing global and local attributions with SHAP\'s KernelExplainer
Identifying influential features with factor prioritization
Quantifying uncertainty and cost sensitivity with factor fixing
Mission accomplished
Summary
Dataset and image sources
References
Section 3:Tuning for Interpretability
Chapter 10: Feature Selection and Engineering for Interpretability
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Understanding the effect of irrelevant features
Reviewing filter-based feature selection methods
Basic filter-based methods
Correlation filter-based methods
Ranking filter-based methods
Comparing filter-based methods
Exploring embedded feature selection methods
Discovering wrapper, hybrid, and advanced feature selection methods
Wrapper methods
Hybrid methods
Advanced methods
Evaluating all feature-selected models
Considering feature engineering
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 11: Bias Mitigation and Causal Inference Methods
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Detecting bias
Visualizing dataset bias
Quantifying dataset bias
Quantifying model bias
Mitigating bias
Pre-processing bias mitigation methods
In-processing bias mitigation methods
Post-processing bias mitigation methods
Tying it all together!
Creating a causal model
Understanding the results of the experiment
Understanding causal models
Initializing the linear doubly robust learner
Fitting the causal model
Understanding heterogeneous treatment effects
Choosing policies
Testing estimate robustness
Adding random common cause
Replacing treatment with a random variable
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 12: Monotonic Constraints and Model Tuning for Interpretability
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Placing guardrails with feature engineering
Ordinalization
Discretization
Interaction terms and non-linear transformations
Categorical encoding
Other preparations
Tuning models for interpretability
Tuning a Keras neural network
Tuning other popular model classes
Optimizing for fairness with Bayesian hyperparameter tuning and custom metrics
Implementing model constraints
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 13: Adversarial Robustness
Technical requirements
The mission
The approach
The preparations
Loading the libraries
Understanding and preparing the data
Loading the CNN base model
Assessing the CNN base classifier
Learning about evasion attacks
Defending against targeted attacks with preprocessing
Shielding against any evasion attack via adversarial training of a robust classifier
Evaluating and certifying adversarial robustness
Comparing model robustness with attack strength
Certifying robustness with randomized smoothing
Mission accomplished
Summary
Dataset sources
Further reading
Chapter 14: What\'s Next for Machine Learning Interpretability?
Understanding the current landscape of ML interpretability
Tying everything together!
Current trends
Speculating on the future of ML interpretability
A new vision for ML
A multidisciplinary approach
Adequate standardization 
Enforcing regulation
Seamless machine learning automation with built-in interpretation
Tighter integration with MLOps engineers
Further reading
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