Christophm Interpretable Machine Learning Book
The “Christophm Interpretable Machine Learning Book” delves into the realm of interpretable machine learning, aiming to demystify complex models and make their decisions understandable to humans. Drawing from a wealth of knowledge on AI applications, the book explores techniques to enhance transparency and trust in machine learning systems. By leveraging insights from various sources, it provides a comprehensive guide on how to interpret and explain the inner workings of sophisticated algorithms. Through a blend of practical examples and theoretical concepts, this book equips readers with the tools to navigate the intricate landscape of interpretable machine learning effectively.
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Bike Rentals (Regression)
This dataset contains daily counts of rented bicycles from the bicycle rental company Capital-Bikeshare in Washington D.C., along with weather and seasonal information. The data was kindly made openly...
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R Packages Used
arules . Hahsler M, Buchta C, Gruen B, Hornik K (2023). arules: Mining Association Rules and Frequent Itemsets . R package version 1.7-7, https://CRAN.R-project.org/package=arules . bookdown . Xie Y (...
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The Future of Interpretability
Let us take a look at the possible future of machine learning interpretability. The focus will be on model-agnostic interpretability tools. It is much easier to automate interpretability when it is de...
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Detecting Concepts
Author: Fangzhou Li @ University of California, Davis So far, we have encountered many methods to explain black box models through feature attribution. However, there are some limitations regarding th...
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Risk Factors for Cervical Cancer (Classification)
The cervical cancer dataset contains indicators and risk factors for predicting whether a woman will get cervical cancer. The features include demographic data (such as age), lifestyle, and medical hi...
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Evaluation of Interpretability
There is no real consensus about what interpretability is in machine learning. Nor is it clear how to measure it. But there is some initial research on this and an attempt to formulate some approaches...
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Pixel Attribution (Saliency Maps)
Pixel attribution methods highlight the pixels that were relevant for a certain image classification by a neural network. The following image is an example of an explanation: You will see later in thi...
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Story Time
We will start with some short stories. Each story is an admittedly exaggerated call for interpretable machine learning. If you are in a hurry, you can skip the stories. If you want to be entertained a...
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Datasets
Throughout the book, all models and techniques are applied to real datasets that are freely available online. We will use different datasets for different tasks: Classification, regression and text cl...
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Functional Decomposition
A supervised machine learning model can be viewed as a function that takes a high-dimensional feature vector as input and produces a prediction or classification score as output. Functional decomposit...
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Interpretable Models
The easiest way to achieve interpretability is to use only a subset of algorithms that create interpretable models. Linear regression, logistic regression and the decision tree are commonly used inter...
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Terminology
To avoid confusion due to ambiguity, here are some definitions of terms used in this book: An Algorithm is a set of rules that a machine follows to achieve a particular goal 2 . An algorithm can be co...
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