Interpretable machine learning a guide for making black box models explainable
| Otros Autores: | |
|---|---|
| Formato: | Libro |
| Idioma: | Inglés |
| Publicado: |
München, Germany :
Christoph Molnar
2022.
|
| Edición: | Second edition |
| Materias: | |
| Ver en Universidad de Deusto: | https://oceano.biblioteca.deusto.es/primo-explore/search?query=any,contains,991006802971603351&tab=default_tab&search_scope=deusto_alma&vid=deusto |
Solicitar por préstamo interbibliotecario:
Correo
| Notas: | "After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks"--Cubierta posterior. |
|---|---|
| Descripción Física: | X, 317 páginas : ilustraciones, gráficos (blanco y negro y color) ; 25 cm |
| Bibliografía: | Bibliografía: páginas 309-318 (sin numerar). |
| ISBN: | 9798411463330 |
