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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.
This book attempts to make AI models explainable to help developers increase the adoption of AI-based models within their organizations and bring more transparency to decision-making. After reading this book, you will be able to use Python libraries such as Alibi, SHAP, LIME, Skater, ELI5, and CAPTUM. Explainable AI Recipes provides a problem-solution approach to demonstrate each machine learning model, and shows how to use Python’s XAI libraries to answer questions of explainability and build trust with AI models and Machine Learning models. All source code can be downloaded from github.com.
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