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

Assessing time series models with traditional interpretation methods

A time series regressor model can be evaluated as you would evaluate any regression model; that is, using metrics derived from mean square error or the r-squared score. There are, of course, cases in which you will need to use a metric with medians, logs, deviances, or absolute values. These models don't require any of this.

Using standard regression metrics

The evaluate_reg_mdl function can evaluate the model, output some standard regression metrics, and plot them. The parameters for this model are the fitted model (lstm_traffic_672_mdl), X_train (gen_train_672), X_test (gen_test_672), y_train, and y_test.

Optionally, we can specify a y_scaler so that the model is evaluated with the labels inverse transformed, which makes the plot and root mean square error (RMSE) much easier to interpret. Another optional parameter that is very much necessary, in this case, is y_truncate=True because our y_train and...