Interpretable Machine Learning, 2nd Edition: A Guide for Making Black Box Models Explainable
Overview & Synopsis
This book covers a range of interpretability methods, from inherently interpretable models to methods that can make any model interpretable, such as SHAP, LIME and permutation feature importance.
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Document Information
| Document Title | Interpretable Machine Learning, 2nd Edition: A Guide for Making Black Box Models Explainable |
|---|---|
| Author / Organization | Christoph Molnar |
| Subject & Discipline | Machine Learning, Deep Learning and Neural Networks, Artificial Intelligence, Statistics, Mathematical Statistics, and SAS Programming, Probability and Stochastic Processes |
| Categories | Machine Learning |
| Publication Year | 2022 |
| ISBN | 979-8411463330 |
| License / Rights | Creative Commons License (CC) |
| Resource Verification | PDF Unavailable at Remote Host |
| Alternative Mirrors & Links | |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Language | ENGLISH |
| Catalog ID | DOC-9723 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |
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