Interpretable machine learning with Python : learn to build interpretable high-performance models with hands-on real-world examples / Serg Masís.

Format:

Book

Author/Creator:

Masís, Serg, author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (xvi, 715 pages) : illustrations

Place of Publication:

Birmingham, England ; Mumbai : Packt, [2021]

Biography/History:

Masis Serg: Serg Masis has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-makingand machine learning interpretation helps bridge this gap robustly.

Summary:

This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.

Contents:

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

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

Chapter 3: Interpretation Challenges

The preparations

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

Dataset sources

Section 2: Mastering Interpretation Methods

Chapter 4: Fundamentals of Feature Importance and Impact

Personality and birth order

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

Chapter 5: Global Model-Agnostic Interpretation Methods

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

Chapter 6: Local Model-Agnostic Interpretation Methods

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

Chapter 7: Anchor and Counterfactual Explanations

Unfair bias in recidivisim risk assessments

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

Chapter 8: Visualizing Convolutional Neural Networks

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

Bonus method: SHAP's DeepExplainer

Dataset and image sources

Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

The preparation.

Loading the libraries

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

References

Section 3: Tuning for Interpretability

Chapter 10: Feature Selection and Engineering for Interpretability

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

Chapter 11: Bias Mitigation and Causal Inference Methods

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

Chapter 12: Monotonic Constraints and Model Tuning for Interpretability

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

Chapter 13: Adversarial Robustness

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

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.

Notes:

Description based on print version record.

ISBN:

1-80020-657-7

OCLC:

1244620777