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Explainable AI - SHAP Global Interpretability and Random Forest in Python
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https://www.youtube.com/watch?v=q7TQzFpfc5o https://colab.research.google.com/drive/1XX3VUNSXRWHzjD8nnRCs6Olj1WXkCjEL?usp=sharing https://linktr.ee/diogoalvesderesende In the second video of the series, we use SHAP to explain a Random Forest Model. SHAP is one of the most exciting techniques to explain AI. The video focuses on the Global interpretability power of SHAP. That is, we look at the predictors and how they influence the prediction. This is a hands-on step by step guide on how to code in Python and Google Colab Random Forest, an advanced Artificial Intelligence/Machine learning model, and as well a tutorial on programming SHAP. Intro 0:00 Importing libraries 0:51 Importing Data 1:48 Transforming String Variables 2:24 Isolate X and Y 3:05 Training and Test set 3:52 Random Forest 5:21 Preparing SHAP 6:20 SHAP Global Interpretability 6:58 Interpreting the Visualization 8:34 Outro 10:18
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