Kaggle 30 Days of ML (Day 19) - Understanding SHAP Summary Plot - Interpretable Machine Learning

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This is the Day 19 of Kaggle's 30 Days of ML Challenge where you can learn Machine Learning (based on Python) in 30 days (Kind of). It's not a competition but a challenge to make a habit of coding ML every day. If you haven't registered for the Kaggle Challenge Don't worry, You can follow along my videos every day.

In this Machine Learning Explainability Video, We'll learn about SHAP & Shapley Values.

✅SHAP Recap
✅SHAP Summary Plots
✅SHAP Dependence Plot
✅Wrap up

#kaggle #30daysofml #machinelearning #datascience

1littlecoder

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I have applied Shap for my loan default model (decision tree, random forest, Xg boost) however, when I am running the code for decision tree it takes 34 mins for 252000 rows. Random forest it is not working and kept on running for more than 2 hrs after which I stopped. What to do?

pragatiganguly

Need some help o SHAP decision_plot as it gives an error "ValueError: The base_values and shap_values args expect lists".

bd

Kaggle 30 Days of ML (Day 19) - Understanding SHAP Summary Plot - Interpretable Machine Learning

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