Question regarding generating Decision tree using RapidMiner tool
Hello everyone,
I have a question regarding on how to properly generate a decision tree using rapid miner tool. This question is related on picking the right label attribute, and as well as on how to actually generate a tree which makes sense? I've got a specific data set which I load into the "Read excel" operator, pick the label attribute I want, which I connect then to the "Decision tree" operator in rapid miner. This is how it looks like in the end:
But the resulting decission tree is either too small, too big or its not showing at all what I wanted to represent it... Is there any way that I can "force" the algorithm to branch off each time on specific column I tell it to? Something like this:
If the outlook is overcast, person X will play golf. If the outlook is rain, but if its windy, person X won't play golf, otherwise person X will play golf.
I'm quite new with data mining, and every explanation would be really nice on how can I generate a proper decision tree that will actually look like something that is readable...
Thanks a lot!
I have a question regarding on how to properly generate a decision tree using rapid miner tool. This question is related on picking the right label attribute, and as well as on how to actually generate a tree which makes sense? I've got a specific data set which I load into the "Read excel" operator, pick the label attribute I want, which I connect then to the "Decision tree" operator in rapid miner. This is how it looks like in the end:
But the resulting decission tree is either too small, too big or its not showing at all what I wanted to represent it... Is there any way that I can "force" the algorithm to branch off each time on specific column I tell it to? Something like this:
If the outlook is overcast, person X will play golf. If the outlook is rain, but if its windy, person X won't play golf, otherwise person X will play golf.
I'm quite new with data mining, and every explanation would be really nice on how can I generate a proper decision tree that will actually look like something that is readable...
Thanks a lot!