Explainable AI Recipes : Implement Solutions to Model Explainability and Interpretability with Python / by Pradeepta Mishra.

Format:

Book

Author/Creator:

Mishra, Pradeepta, author.

Language:

English

Subjects (All):

Artificial intelligence.

Python (Computer program language).

Artificial Intelligence.

Python.

Local Subjects:

Artificial Intelligence.

Python.

Physical Description:

1 online resource (272 pages)

Edition:

1st ed. 2023.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2023.

Summary:

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. You will: 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.

Contents:

Chapter 1: Introduction to Explainability Library Installations

Chapter 2: Linear Supervised Model Explainability

Chapter 3: Non-Linear Supervised Learning Model Explainability

Chapter 4: Ensemble Model for Supervised Learning Explainability

Chapter 5: Explainability for Natural Language Modeling

Chapter 6: Time Series Model Explainability

Chapter 7: Deep Neural Network Model Explainability.

Notes:

Includes index.

Includes bibliographical references and index.

Other Format:

Print version: Mishra, Pradeepta Explainable AI Recipes

ISBN:

9781484290293

1484290291

OCLC:

1370607273