Book Image

Interpretable Machine Learning with Python

By : Serg Masís
Book Image

Interpretable Machine Learning with Python

By: Serg Masís

Overview of this book

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Table of Contents (19 chapters)
1
Section 1: Introduction to Machine Learning Interpretation
5
Section 2: Mastering Interpretation Methods
12
Section 3:Tuning for Interpretability

Understanding heterogeneous treatment effects

Firstly, it's important to note how the DoWhy wrapper of econml has cut down on a few steps with the dowhy.fit method. Usually, when you build a CausalModel such as this one directly with DoWhy, it has a method called identify_effect that derives the probability expression for the effect to be estimated (the identified estimand). In this case, this is called the average treatment effect (ATE). Then, another method called estimate_effect takes this expression and the models it's supposed to tie together (regression and propensity). With them, it computes both the ATE, , and CATE, , for every outcome and treatment . However, since we used the wrapper to fit the causal model, it automatically takes care of both the identification and estimation steps.

You can access the identified ATE with the identified_estimand_ property and the estimate results with the estimate_ property for the causal model. The code can be seen in the following...