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Ferdi
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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.

enter image description here

Thanks in advance!

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.

enter image description here

Thanks in advance!

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.

enter image description here

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Jasam
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H2o interpretability - LIME

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.

enter image description here

Thanks in advance!