You often heard this metaphor that the modern machine learning systems are “black boxes” and what that means is that we humans aren’t capable of gaining any insights on how the ML system work exactly.
The field of machine learning is growing daily. The AI-powered applications have become an ever-increasing part of our lives, from facial recognition systems to autonomous machines, and personalized systems.
When choosing a suitable ML model, we often think in terms of accuracy vs. interpretability trade-off.
The Black-box models such as neural networks, gradient boosting models or complicated ensembles often provide great accuracy. The inner workings of these models are harder to understand and they don’t provide an estimate of the importance of each feature on the model predictions, and also it’s now easy to understand how all the different features interact.
Whereas the simpler models such as linear regression and decision trees, on the other hand, provide less predictive capacity and are not always capable of modeling the inherent complexity of the dataset (i.e. feature interactions). They are however significantly easier to explain and interpret.
This sort of decisions and predictions being made by these machine learning-enabled systems are becoming much more profound, and in many cases, critical to life, death, and personal wellness.
Model Interpretability is very useful regardless of what problem you are solving, it creates reliability, makes debugging quick and easy, adding informing feature, directing future data collection, informing human decision-making and building Trust.
In this book called An Introduction To Machine Learning Interpretability, you will get a quick guide to what Machine Learning Interpretability is and how it works and what are its features.
The writers of this ebook have thoroughly introduced the idea of machine learning interpretability and examine a set of machine learning techniques, algorithms, and models to help data scientists improve the accuracy of their predictive models while maintaining interpretability.
Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand.
When accuracy outpaces interpretability, human trust suffers, affecting business adoption, regulatory oversight, and model documentation. Also, industries like banking, insurance, and healthcare, in particular, require predictive models that are interpretable.
This ebook will cover topics like how machine learning and predictive modeling are applied in practice.
The book also has a broad chapter about the social and commercial motivations for machine learning interpretability, fairness, accountability, and how the transparency is made.
An Introduction To Machine Learning Interpretability, also talk about the differences between linear models and more accurate machine learning models, which is very useful to understand ML Interpretability
Linear models, as well as tree-based models, can be easily interpreted because of their intuitive way of getting to the predictions, but you might need to sacrifice on accuracy since these models are simple and can easily under- or overfit depending on the problem.
The book also gets a definition of interpretability and learn about the groups leading interpretability research and it also examines a taxonomy for classifying and describing interpretable machine learning approaches.
As per approach, there are several practical techniques for data visualization, training interpretable machine learning models, and generating explanations for complex model predictions
Also, there is a brief talk about where you can explore automated approaches for testing model interpretability.
The book covers topics like commercial motivations, enhancing established analytical processes, regulatory compliance, adoption and acceptance techniques and methods.
It also talks about the machine Learning Interpretability Taxonomy for Applied Practitioners and it’s Limitations and Precautions. I also like the section about Testing Interpretability and Fairness and also Machine Learning Interpretability in Action.
Table of Contents
An Introduction to Machine Learning Interpretability
Machine Learning and Predictive Modeling in Practice
Social and Commercial Motivations for Machine Learning Interpretability
The Multiplicity of Good Models and Model Locality
Accurate Models with Approximate Explanations
A Machine Learning Interpretability Taxonomy for Applied Practitioners
A Scale for Interpretability
Global and Local Interpretability
Model-Agnostic and Model-Specific Interpretability
Understanding and Trust
Common Interpretability Techniques
Seeing and Understanding Your Data
Techniques for Creating White-Box Models
Techniques for Enhancing Interpretability in Complex Machine Learning Models
Sensitivity Analysis: Testing Models for Stability and Trustworthiness
Machine Learning Interpretability in Action
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