01-07-2017 08:14 AM
I made a classification model using the decison tree. But when i apply it, it gives me the same prediction with the same confidence level, for every example, like in this picture i posted. Can anyone tell which mistake could cause thisto happen? Thank you.
01-07-2017 08:56 AM
can you have alook at your tree? Could it be that it simply does not split? What happens if you deactivate pruning and prepruning in the tree?
01-07-2017 09:47 AM
I can't tell from your snapshot but does your label column contain all missing values?
01-08-2017 08:52 AM - edited 01-08-2017 08:54 AM
Thank you for your answer. I will try that.
Here you can see how my process looks like, and decesion tree as well. I'm trying to do text mining but i am a beginer, so i don't know too much about it.
First i tried to run a process without connecting wordlist from the first Process Docs from Data operator to the second one, and then i've got an error message that says- atributes dont match. And then i connected those two, so now i have a problem that made me come here
01-08-2017 11:26 AM
According to what you show, your Decision Tree doesn't split. Wrap that DT into a Cross Validation operator and measure hte performance. My guess is that it'll classify the majority of your "0" class incorrectly.
01-08-2017 01:49 PM
just google for decision tree and pruning. Your tree got simply too much pruned. Most likely you need to reduce the min_gain to 0.001.