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Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. 


The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution.   

After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.


What You Will Learn
  • Create code snippets and explain machine learning models using Python
  • Leverage deep learning models using the latest code with agile implementations
  • Build, train, and explain neural network models designed to scale
  • Understand the different variants of neural network models 
Who This Book Is For

AI engineers, data scientists, and software developers interested in XAI

Table of Contents

  1. Cover
  2. Front Matter
  3. 1. Introducing Explainability and Setting Up Your Development Environment
  4. 2. Explainability for Linear Supervised Models
  5. 3. Explainability for Nonlinear Supervised Models
  6. 4. Explainability for Ensemble Supervised Models
  7. 5. Explainability for Natural Language Processing
  8. 6. Explainability for Time-Series Models
  9. 7. Explainability for Deep Learning Models
  10. Back Matter
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