Model Explainability Explained: A Human’s Guide to Building Trust in Data Science
Here’s how to understand model explainability.
Machine learning models are changing the way that every industry does business today. But the models that have such potential to impact your business can also be hard to understand and explain. That, in turn, makes it hard for you to get buy-in from leadership and limits the ROI of your data science projects.
So what’s the solution? You need to interpret and explain your models in ways that everyone can understand.
Download this whitepaper to learn how to do just that. In it, we cover:
- Why understanding model explainability is critical for today’s enterprises.
- How to look at both the global and local level to understand your models’ decisions.
- What leadership needs to understand to be fully bought in on ML projects.
- And more!
Your models might be more explainable than you think! With a few changes to how you build and present your models, you’ll be ready to get leadership buy-in and maximize the effectiveness of your machine learning initiatives.