How to build a prediction model by weight
I am Rapidminer beginner. I have the following problem:
I have a small dataset: only 11 rows, but 102 attributes. The label is binominal: 1 or 2.
The decision tree finds only one attribute that discriminates between 1 and 2 in the 11 rows with 100% accuracy - which has a accuracy of about 51% tested on a second validation data set.
Using "Weight by correlation" and by manual visual comaprison of the graphs I was able to find about 6 attributes that discriminate very good between 1 and 2.
Now I want to generade a model out of the top 6 weighted attributes and test it on a unlabled data set.
How do I do this?
here is my process so far