Classification
Hi all,
When classification has been explained to me before it has been in two steps,
Firstly, a learning phase which takes the data set puts it through a classification algorithm, making a model (e.g. a set of rules)
Secondly, the classification phase where test data is used to measure the accuracy of the model.
When I use a classification method such as decision tree on RapidMiner, does it do both of these steps in 1 component? Or am I doing the second phase when using cross-validation etc.?
Thanks for any help and please correct any flaws in my logic.
-Madcap
When classification has been explained to me before it has been in two steps,
Firstly, a learning phase which takes the data set puts it through a classification algorithm, making a model (e.g. a set of rules)
Secondly, the classification phase where test data is used to measure the accuracy of the model.
When I use a classification method such as decision tree on RapidMiner, does it do both of these steps in 1 component? Or am I doing the second phase when using cross-validation etc.?
Thanks for any help and please correct any flaws in my logic.
-Madcap