Details zum E-Book

Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples

Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples

Serg Masís

E-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.
  • 1. Interpretation, Interpretability and Explainability; and why does it all matter?
  • 2. Key Concepts of Interpretability
  • 3. Interpretation Challenges
  • 4. Fundamentals of Feature Importance and Impact
  • 5. Global Model-Agnostic Interpretation Methods
  • 6. Local Model-Agnostic Interpretation Methods
  • 7. Anchor and Counterfactual Explanations
  • 8. Visualizing Convolutional Neural Networks
  • 9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
  • 10. Feature Selection and Engineering for Interpretability
  • 11. Bias Mitigation and Causal Inference Methods
  • 12. Monotonic Constraints and Model Tuning for Interpretability
  • 13. Adversarial Robustness
  • 14. What's Next for Machine Learning Interpretability?
  • Titel: Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
  • Autor: Serg Masís
  • Originaler Titel: Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
  • ISBN: 9781800206571, 9781800206571
  • Veröffentlichungsdatum: 2021-03-26
  • Format: E-book
  • Artikelkennung: e_2a3q
  • Verleger: Packt Publishing