I am new to data analytics having only started exploring the field this week. I have downloaded KNIME and am working with a single dataset to try out different classification algorithms.
I am currently trying out the decision tree algorithm and would like to include cross validation. Currently I partition the dataset 50/50 with the training data going to the learner node and the test to the predictor. Now for the part where I need you to help my understanding. If I want to use the cross validation node in KNIME to estimate the test data error rate, do I still need to partition the data before giving it to the cross-partitioner node?
Initially I assumed I needed to as my understanding of the testing set is that it is used to test the models classification ability by using records that are not in the training data, and with cross validation all of the records in the dataset are used as both training and test data at least once. However I have since seen the metaworkflow cross validation example here https://www.knime.org/introduction/examples.
Any advice to help clear up my confusion would be greatly appreciated.