num_pages: 736
description: Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.The first section of the book is a beginner's guide to interpretability covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work compare them to black-box and glass-box models and examine their trade-off. The second section will 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 the book also helps the reader to interpret model outcomes using examples. In the third section youll get hands-on with tuning models and training data for interpretability by reducing complexity mitigating bias placing guardrails and enhancing reliability. The methods youll 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.
language: English
title: Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples
publisher: Packt Publishing