Explainable AI recipes: implement solutions to model explainability and interpretability with Python
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 in...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[California]
Apress
[2023]
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484290293/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes index. - Print version record |
| Umfang: | 1 Online-Ressource (253 Seiten) illustrations (black and white, and colour). |
| ISBN: | 9781484290293 1484290291 |
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| spelling | Mishra, Pradeepta VerfasserIn aut Explainable AI recipes implement solutions to model explainability and interpretability with Python Pradeepta Mishra [California] Apress [2023] 1 Online-Ressource (253 Seiten) illustrations (black and white, and colour). Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Print version record 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. Artificial intelligence Python (Computer program language) Intelligence artificielle Python (Langage de programmation) artificial intelligence 1484290283 Erscheint auch als Druck-Ausgabe 1484290283 |
| spellingShingle | Mishra, Pradeepta Explainable AI recipes implement solutions to model explainability and interpretability with Python Artificial intelligence Python (Computer program language) Intelligence artificielle Python (Langage de programmation) artificial intelligence |
| title | Explainable AI recipes implement solutions to model explainability and interpretability with Python |
| title_auth | Explainable AI recipes implement solutions to model explainability and interpretability with Python |
| title_exact_search | Explainable AI recipes implement solutions to model explainability and interpretability with Python |
| title_full | Explainable AI recipes implement solutions to model explainability and interpretability with Python Pradeepta Mishra |
| title_fullStr | Explainable AI recipes implement solutions to model explainability and interpretability with Python Pradeepta Mishra |
| title_full_unstemmed | Explainable AI recipes implement solutions to model explainability and interpretability with Python Pradeepta Mishra |
| title_short | Explainable AI recipes |
| title_sort | explainable ai recipes implement solutions to model explainability and interpretability with python |
| title_sub | implement solutions to model explainability and interpretability with Python |
| topic | Artificial intelligence Python (Computer program language) Intelligence artificielle Python (Langage de programmation) artificial intelligence |
| topic_facet | Artificial intelligence Python (Computer program language) Intelligence artificielle Python (Langage de programmation) artificial intelligence |
| work_keys_str_mv | AT mishrapradeepta explainableairecipesimplementsolutionstomodelexplainabilityandinterpretabilitywithpython |