Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.
|Pages :||: 318 pages|
|File type :|
|License:||CC BY-NC-SA 4.0|
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This tutorial is designed for Machine learning students who are completely unaware of Machine learning concepts but they have basic understanding on Others training.
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