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

Mission accomplished

To approach this mission, you have reduced overfitting using primarily the toolset of feature selection. The non-profit is pleased with a profit lift of roughly 30%, costing a total of , which is ,000 less than it would cost to send everyone in the test dataset the mailer. However, they still want assurance that they can safely employ this model without worries that they'll experience losses.

In this chapter, we've examined how overfitting can cause the profitability curves not to align. Misalignment is critical because it could mean that choosing a threshold based on training data would not be reliable on out-of-sample data. So, you use compare_df_plots to compare profitability between the test and train sets as you've done before, but this time for the chosen model (rf_5_e-llarsic):

profits_test = reg_mdls['rf_5_e-llarsic']['profits_test']
profits_train = reg_mdls['rf_5_e-llarsic']['profits_train']...