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Decision Trees
Contributor I
I'm new to data mining and just trying to figure out how it works. I've started with Decision Trees for binomial classification.
One thing that I have found that has surprised me is that the tree can change quite a bit as I add to my data set. Even if I only add examples that will all predict negative, the tree still changes. My overall sense is that the tree becomes more arbitrary, even after pruning, but that might only be my current project. People around me who see the variability of the tree structure are immediately full of doubts about the decisions that are rendered. I can't say I blame them.
Can someone explain what is going on, in layman's terms?
One thing that I have found that has surprised me is that the tree can change quite a bit as I add to my data set. Even if I only add examples that will all predict negative, the tree still changes. My overall sense is that the tree becomes more arbitrary, even after pruning, but that might only be my current project. People around me who see the variability of the tree structure are immediately full of doubts about the decisions that are rendered. I can't say I blame them.
Can someone explain what is going on, in layman's terms?
0
Answers
defining a max depth of the tree could help. To check how well your data is represented or just (over) fitted you may also apply an X-Validation operator.
Happy mining !
I need little bit help in decision tree. I was importing csv to rapidminer and get a tree for the Titanic project, but there is missing something. Can someone help me please?
Thank you!