I have trained a model to predict heart attacks using random forest algorithm using H2O.
I have good performance in cross validation.
Now, I want to give more interpretation to the predictions in a test set, I used Lime and I followed this tutorial: https://kkulma.github.io/2017-11-07-automated_machine_learning_in_cancer_detection/
Everything works fine, now the issue is that I want to understand each explanation for each case, which means:
- What means Support?
- What means Contradicts?
- Interpret each rule given by the explanation example (2800 < dias_patologia_diabetes).
- What means explanation fit?
Attached the plot of 2 cases (those that have) more probability.