AI Simplified: SHAP Values in Machine Learning
What are SHAP values and how are they being used to explain machine learning predictions?
Meet Mark Romanowsky, a Data Scientist at DataRobot. Mark explains the important role that SHAP values play in machine learning by providing real-world examples: how a group of friends can fairly and efficiently share an Uber ride to their separate homes, and helping college students at risk of not graduating.
In everyday life, Shapley values are a way to fairly split a cost or payout among a group of participants who may not have equal influence on the outcome. In machine learning models, SHAP values are a way to fairly assign impact to features that may not have equal influence on the predictions.
Learn more in his AI Simplified video:
Ready for more AI Simplified? Check out these other videos:
- AI Simplified: Model Evaluation
- AI Simplified: Trusting Your AI
- AI Simplified: Model Interpretability
Thanks! Check your inbox to confirm your subscription.
We’re almost there! These are the next steps:
- Look out for an email from DataRobot with a subject line: Your Subscription Confirmation.
- Click the confirmation link to approve your consent.
- Done! You have now opted to receive communications about DataRobot’s products and services.
Didn’t receive the email? Please make sure to check your spam or junk folders.
-
Five Steps to Reduce Customer Churn and Increase Revenue
April 13, 2021· 3 min read -
DataRobot Automated Feature Discovery
April 12, 2021· 5 min read -
A Space-Focused Start-up Takes Aim at Space Junk
April 12, 2021· 4 min read
Latest posts
Related Posts
