Open the Black Box: an Introduction to Model Interpretability with LIME and SHAP - Kevin Lemagnen

[@maheshmm2]

3:33 : github and colab links to code 5:20 : why is it important? Data bias 12:42: Explain like i'm 5 14:19 : Introduction to Interoperability (Jupyter code) 15:38 : sklearn.compose import column.transformer 20:55 : train, test 21.41: white box models, logistic regression 30:00: probability , score explained. 35.27 : Decision tree 36.28 : LIME 45:17 : LIME API 46:00: Random Forest 60:23 : SHAP 64:05 : SHAP API 75:31 : no tabular data 83:00 : Conclusion

[@narotian]

I appreciate that well explained.

[@bryanparis7779]

55:58 According to LIME, do these blue contributions of features really sum up to the probability of 0.71, if we show all contributions? Similarly, the orange ones are we sure that sum up to 0.29? I have examples of making me confused about this....