Understanding and trusting models and their results is a hallmark of good science. Analysts, engineers, physicians, researchers, scientists, and humans in general have the need to understand and trust models and modeling results that affect our work and our lives.
Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). But, tools now exist to build accurate and sophisticated modeling systems based on heterogeneous data and machine learning algorithms and to enable human understanding and trust in these complex systems. In short, you can now have your accuracy and interpretability cake…and eat it too.
Download this book to learn to make the most of recent and disruptive breakthroughs in debugging, explainability, fairness, and interpretability techniques for machine learning. In this report you’ll find:
- Definitions and examples
- Social and Commercial Motivations for Machine Learning
- A Machine Learning Interpretability Taxonomy for Applied Practitioners
- Common Interpretability Techniques
- Limitations and Precautions
- Testing Interpretability and Fairness
- Machine Learning Interpretability in Action