Understand Tree Decision


I am starting with Knime and I have created a workflow to evaluate different calcification algorithms. For my data, the best is Tree because a I have a good accuracy.
My question is related about how can I understand the algorithm, and what are the fields of my data that have more influence in the prediction.
I am trying to determine if a customer is going to leave the company and I have 50 fields in my database, but I don´t know what are more important fields to decide that a customers is going to leave.

Is there any way to decide this?


I recently wrote this entry. Maybe the links there might help you explore more about machine learning

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Thank you for you answer.
I need to analyze better but as I understand this workflow compare two models but when you have selected a models, it´s possible to know what fields have more weight in the decision?


Hello @AnaBerta,

and welcome to KNIME and it’s Community!

Sure there is. In case of Decision Tree algorithm (which has tree structure as output) the top-level features are the more important that the others.

Check this topic with further topics linked for more about it:



Thank you for your answer. I am going to test.


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