Copy

Explaining "Blackbox" ML Models - Practical Application of SHAP

GBM models have been battle-tested as powerful models but have been tainted by the lack explainability. Typically data scientists look at variable importance plots but they are not enough to explain how a model works. To maximize adoption by the model user, use SHAP values to answer common explainability questions and build trust in your models.

In this post, we will train a GBM model on a simple dataset and you will learn how to explain how the model works. The goal here is not to explain how the math works, but to explain to a non-technical user how the input variables are related to the output variable and how predictions are made.

Read on Github

Questions?

To learn more about the DataBolt tools and products that help you accelerate data science, check out www.databolt.tech

To see other blog posts check out our archive at blog.databolt.tech.

For questions and feedback email us at support@databolt.tech

Share
Tweet
Forward
Copyright © 2020 www.databolt.tech, All rights reserved.


Want to change how you receive these emails?
You can update your preferences or unsubscribe from this list.

Email Marketing Powered by Mailchimp