interpretable-ml-book
Explaining the decisions and behaviour of machine learning models.
You can find the current version of the book here: https://christophm.github.io/interpretable-ml-book/
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. In an ideal future, machines will be able to explain their decisions and make a transition into an algorithmic age more human. This books is recommended for machine learning practitioners, data scientists, statisticians and also for stakeholders deciding on the use of machine learning and intelligent algorithms.
The book is automatically build from the master branch and pushed to gh-pages by Travis CI.
Clone the repository.
git clone git@github.com:christophM/interpretable-ml-book.git
Make sure all dependencies for the book are installed. This book has the structure of an R package, so dependencies can be installed easily, only R and the devtools library is required. Start an R session in the folder of the book repository and type:
devtools::install_deps()
For rendering the book, start an R session and type:
setwd("manuscript") # first, generate the references source("../scripts/references.R") bookdown::render_book('', 'bookdown::gitbook')
After rendering, the HTML files of the book will be in the "_book" folder. You can either double-click index.html directly or, of course, do it in R:
browseURL('_book/index.html')
Export from Leanpub in 7.44" x 9.68" 18.9cm x 24.6cm
For cover: 7.565 x 9.925", 19.226 x 25.224cm, see recommended sizes
Font for front cover: Francois One
Stuff that both works for leanpub and for bookdown:
Titles start with #, subtitles with ## and so on.
Titles can be tagged using {#tag-of-the-title}
Chapters can be referenced by using [text of the link](#tag-of-the-title)
Figures can be referenced by using [text of the link](#fig:tag-of-r-chunk-that-produced-figure)
Start and end mathematical expressions with $
(inline) or with $$
(extra line). Will be automatically changed for leanpub with a regexpr. Conversion script only works if no empty spaces are in the formula.
Leave empty lines between formulas and text (if formula not inline)
References have to be writen like this: [^ref-tag]
and must be at the end of the respective file with [^ref]: Details of the reference ...
. Make sure the space is included. References are collected in 10-reference.Rmd with the script references.R. Make sure not to use [^ref-tag]:
anywhere in the text, only at the bottom for the actual reference.
Printing for proofreading with extra line spacing: Build HTML book, go to manuscript/_book/libs/gitbook*/css/style.css, change line-height:1.7 to line-height:2.5, open local html with chrome, print to pdf with custom margin.
All notable changes to the book will be documented here.
Started section on neural network interpretation
Added chapter on feature visualization
Added SHAP chapter
Added Anchors chapter
Fixed error in logistic regression chapter: Logistic regression was predicting class "Healthy", but interpretation in the text was for class "Cancer". Now regression weights have the correct sign.
Renamed Feature Importance chapter to "Permutation Feature Importance"
Errata:
Chapter 4.3 GLM, GAM and more: Logistic regression uses logit, not logistic function as link function.
Chapter Linear models: Formula for adjusted R-squared was corrected
Fixes wrong index in Cooks Distance summation (i -> j)
fixed boxplot formula (1.5 instead of 1.58)
Change to colorblind-friendly color palettes (viridis)
Make sure plots work in black and white as well
Extensive proofreading and polishing
Renamed Definitions chapter to Terminology
Added mathematical notation to Terminology (former Definitions) chapter
Added LASSO example
Restructured lm chapter and added pros/cons
Renamed "Criteria of Interpretability Methods" to "Taxonomy of Interpretability Methods"
Added advantages and disadvantages of logistic regression
Added list of references at the end of book
Added images to the short stories
Added drawback of shapley value: feature have to be independent
Added tree decomposition and feature importance to tree chapter
Improved explanation of individual prediction in lm
Added "What's Wrong With my Dog" example to Adversarial Examples
Added links to data files and pre-processing R scripts
Added chapter on accumulated local effects plots
Added some advantages and disadvantages to pdps
Added chapter on extending linear models
Fixed missing square in the Friedman H-statistic
Added discussion about training vs. test data in feature importance chapter
Improved the definitions, also added some graphics
Added an example with a categorical feature to PDP
Added chapter on influential instances
Added chapter on Decision Rules
Added chapter on adversarial machine examples
Added chapter on prototypes and criticisms
Added chapter on counterfactual explanations
Added section on LIME images (by Verena Haunschmid)
Added section on when we don't need interpretability
Renamed chapter: Human-style Explanations -> Human-friendly Explanations
Added chapter on global surrogate models
Added improved Shapley pictograms
Added acknowledgements chapter
Added feature interaction chapter
Improved example in partial dependence plot chapter
The weights in LIME text chapter where shown with the wrong words. This has been fixed.
Improved introduction text
Added chapter about the future of interpretability
Added Criteria for Intepretability Methods
Reworked the Feature Importance Chapter
Added third short story
Removed xkcd comic
Merged introduction and about the book chapters
Addeds pros & cons to pdp and ice chapters
Started using the iml package for plots in ice and pdp
Restructured the book files for Leanpub
Added a cover
Added some CSS for nicer formatting
Added chapter about Shapley value explanations
Added short story chapters
Added donation links in Preface
Reworked RuleFit with examples and theory.
Interpretability chapter extended
Add chapter on human-style explanations
Making it easier to collaborate: Travis checks if book can be rendered for pull requests
First release of the Interpretable Machine Learning book
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