The problem that brings me here today should not be very complicated, but I can not think of any way to solve it.
The question is this: When we use a decision tree it returns a series of information that in my case it would be useful to know. First, it would be useful to know what is the way you are following to make a new classification; and second, I would like to know what is the distribution in that rule and the number of examples of the total that classify me in it. This is possible to see when we place the cursor on the final leaf of the tree on the diagram, but how could I know all this automatically if I want to classify a new example?
have a look at the Decision Tree to Example Set operator which is part of Converters Extension. This should return all the information you want.
You're right, but he does not return the information to me the way I need it. When I want to classify new examples I can not find a way to get all this automatically. I would have to extract this information from the rapidminer and then find where the current conditions coincide with the historical ones so that I can use the information. In other words, I would have to use "get decision tree path" to compare and know exactly where I am.
Hi again Martin:
I do not understand what you want to say. How i can?
I need all this information in a single row. that is, I need to know what rule applies to the classification of the new example, the distribution of said rule, the number of examples that compose it and what proportion is of the total of examples of learning. My question is: Can I only do this if I extract this information separately and take it to excel to link all this on my own?