Interpretable Machine Learning - Christoph Molnar
Jul 31, 2024 · Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and …
Interpretable Machine Learning - Coursera
You will learn how to describe interpretable machine learning and differentiate between interpretability and explainability, explain and implement regression models in Python, and demonstrate knowledge of generalized models in Python.
Interpretability in machine learning (ML) is crucial for high stakes decisions and trou- bleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel
Definitions, methods, and applications in interpretable machine learning
We aim to clarify these concerns by defining interpretable machine learning and constructing a unifying framework for existing methods which highlights the underappreciated role played by human audiences. Within this framework, methods are …
Interpretable Machine Learning: Fundamental Principles and 10 …
Mar 20, 2021 · Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic.
Interpretable Machine Learning - Christoph Molnar
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. It also includes interpretation methods specific to deep neural networks, and discusses why interpretability is important in machine learning.
Interpretable Machine Learning - Christoph Molnar - Google …
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression.
[1901.04592] Interpretable machine learning: definitions, …
Jan 14, 2019 · We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations.
Interpretable Machine Learning (IML) - GitHub Pages
Interpretable Machine Learning (IML) This website offers an open and free introductory course on Interpretable Machine Learning. The course is constructed as self-contained as possible, and enables self-study through lecture videos, PDF slides, exercises (with solutions), and notebooks.
Interpretability in Machine Learning. An Overview
Feb 5, 2024 · Developing interpretable machine learning systems can help businesses in multiple spheres: Enhance Domain Knowledge: Machine learning models not only automate decision-making processes but also generate valuable insight. Data scientists can access this insight by, for example, understanding the relationships in their data through interpretations.
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