Interpretable Machine Learning with Python

Spis treści książki

• Interpretable Machine Learning with Python
• Contributors
• About the author
• About the reviewers
• Packt is searching for authors like you
• Preface
  • Who this book is for
  • What this book covers
  • To get the most out of this book
  • Download the example code files
  • Download the color images
  • Conventions used
  • Get in touch
  • Reviews
• 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?
      • Beware of complexity
      • When does interpretability matter?
      • What are black-box models?
      • What are white-box models?
    • What is explainability?
      • Why and when does explainability matter?
  • 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
      • The data dictionary
      • Data preparation
  • 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
      • The data dictionary
      • Data preparation
  • Reviewing traditional model interpretation methods
    • Predicting minutes delayed with various regression methods
    • Classifying flights as delayed or not delayed with various classification methods
      • Training and evaluating the classification models
    • Visualizing delayed flights with dimensionality reduction methods
  • Understanding limitations of traditional model interpretation methods
  • Studying intrinsically interpretable (white-box) models
    • Generalized Linear Models (GLMs)
      • Linear regression
      • Ridge regression
      • Polynomial regression
      • Logistic regression
    • Decision trees
      • CART decision trees
    • RuleFit
    • Nearest neighbors
      • k-Nearest Neighbors
    • Nave Bayes
      • Gaussian Nave Bayes
  • Recognizing the trade-off between performance and interpretability
    • Special model properties
      • The key property: explainability
      • The remedial property: regularization
    • Assessing performance
  • Discovering newer interpretable (glass-box) models
    • Explainable Boosting Machine (EBM)
      • Global interpretation
      • Local interpretation
      • Performance
    • Skoped Rules
      • Global interpretation
      • Local interpretation
      • Performance
  • 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
      • The data dictionary
      • Data preparation
  • 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
      • The data dictionary
      • Data preparation
      • Modeling and performance evaluation for deep neural networks
      • Modeling and performance evaluation for XGBoost
  • 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
    • Fitting surrogates
      • Evaluating surrogates
      • Interpreting 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
      • The data dictionary
      • Data preparation
  • Leveraging SHAPs KernelExplainer for local interpretations with SHAP values
    • Training a C-SVC model
      • Computing SHAP values using KernelExplainer
      • Local interpretation for a group of predictions using decision plots
      • Local interpretation for a single prediction at a time using a force plot
  • Employing LIME
    • What is LIME?
      • Local interpretation for a single prediction at a time using LimeTabularExplainer
  • Using LIME for NLP
    • Training a LightGBM model
      • Local interpretation for a single prediction at a time using LimeTextExplainer
  • 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
      • The data dictionary
      • Examining predictive bias with confusion matrices
      • Data preparation
      • Modeling
      • Getting acquainted with our "instance of interest"
  • 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)
      • Configuring WIT
      • Datapoint editor
      • Performance & Fairness
  • 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
      • Loading the CNN model
    • Assessing the CNN classifier with traditional interpretation methods
      • Determining what misclassifications to focus on
  • Visualizing the learning process with activation-based methods
    • Intermediate activations
    • Activation maximization
  • Evaluating misclassifications with gradient-based attribution methods
    • Saliency maps
    • Grad-CAM
      • Creating GradCam++ maps
    • Integrated gradients
      • Using the explainer
    • Tying it all together
  • Understanding classifications with perturbation-based attribution methods
    • Occlusion sensitivity
    • LIME's ImageExplainer
      • 1) Initializing and creating explanations
      • 2) Extracting an image and mask from the explanation
      • 3) Plotting explanations
    • 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
      • The data dictionary
      • Understanding the data
      • Data preparation
      • Loading the LSTM models
  • Assessing time series models with traditional interpretation methods
    • Using standard regression metrics
  • Generating LSTM attributions with integrated gradients
  • Computing global and local attributions with SHAP's KernelExplainer
  • Identifying influential features with factor prioritization
    • Computing Morris sensitivity indices
      • Analyzing the elementary effects
  • Quantifying uncertainty and cost sensitivity with factor fixing
    • Generating and predicting on Salteli samples
      • Performing Sobol sensitivity analysis
      • Incorporating a realistic cost function
  • 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
    • Creating a base model
      • Evaluating the model
      • Training the base model at different max depths
  • Reviewing filter-based feature selection methods
    • Basic filter-based methods
      • Constant features with a variance threshold
      • Quasi-constant features with Value-Counts
      • Duplicating features
      • Removing unnecessary features
    • 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
      • Sequential forward selection (SFS)
      • Sequential Backward Selection (SBS)
    • Hybrid methods
      • Recursive feature elimination
    • Advanced methods
      • Dimensionality reduction
      • Model-agnostic feature importance
      • Genetic algorithms
    • 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
      • The data dictionary
      • Data preparation
  • Detecting bias
    • Visualizing dataset bias
    • Quantifying dataset bias
    • Quantifying model bias
  • Mitigating bias
    • Pre-processing bias mitigation methods
      • Reweighing method
      • Disparate impact remover method
    • In-processing bias mitigation methods
      • Prejudice remover method
      • Gerry fair classifier method
    • Post-processing bias mitigation methods
      • Equalized odds post-processing method
      • Calibrated equalized odds postprocessing method
    • 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
      • Verifying the sampling balance
  • 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
      • Defining the model and parameters to tune
      • Running the hyperparameter tuning
      • Examining the results
      • Evaluating the best model
    • Tuning other popular model classes
      • A quick introduction to relevant model parameters
      • Batch hyperparameter tuning models
      • Evaluating models by precision
      • Assessing fairness for the highest-performing model
    • Optimizing for fairness with Bayesian hyperparameter tuning and custom metrics
      • Designing a custom metric
      • Running Bayesian hyperparameter tuning
      • Fitting and evaluating a model with the best parameters
      • Examining racial bias through feature importance
  • Implementing model constraints
    • Constraints for XGBoost
      • Constraints for TensorFlow Lattice
  • 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
    • Fast Gradient Sign Method attack
      • C&W infinity-norm attack
      • Targeted AP attack
  • 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
  • Why subscribe?
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