Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python 1st Edition Pradeepta Mishra

Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python 1st Edition Pradeepta Mishra – Ebook Instant Download/Delivery ISBN(s): 9781484290293,1484290291

Product details:
• ISBN 10:1484290291
• ISBN 13: 9781484290293
• Author:Pradeepta Mishra

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.
Table contents:

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

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