Testing 2 Techniques for Global Model Explainability скачать в хорошем качестве

Testing 2 Techniques for Global Model Explainability

In this video we will cover 2 different approaches to achieving Global Model Explainability. These include summed SHAP values, and SAGE. After quickly introducing the topic, we'll jump into a Jupyter notebook to experiment with these algorithms on a simple toy dataset. The break-down of this video is as follows: Introduction 00:00 What is Global Model Explainability? 01:02 Jupyter Notebook Setup 04:26 Toy Dataset Creation 05:01 Build Models 07:46 Drive the Model to Make Mistakes 08:46 Test SHAP 10:06 Test SAGE 11:07 Conclusions 12:10 The best way to keep up-to-date with my video/blog content is to sign up for my monthly Newsletter! Please visit: https://insidelearningmachines.com/ne... to register. The notebook presented here can be found at: https://github.com/insidelearningmach... This video is based off of an article on my blog. You can find that blog article here: https://insidelearningmachines.com/gl... The homepage of my blog is: https://insidelearningmachines.com Other social media includes: Twitter:   / inside_machines   Facebook:   / inside-learning-machines-112215488183517   #machinelearning #datascience #xai #shap #sage #python #insidelearningmachines