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:

1. What means Support?
2. What means Contradicts?
3. Interpret each rule given by the explanation example (2800 < dias_patologia_diabetes).
4. What means explanation fit?

Attached the plot of 2 cases (those that have) more probability.

Thanks in advance!