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.
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?