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The updates in 9.9 power advanced use cases and offer productivity enhancements for users who prefer to code.
Performance Vector of Decision Trees
I think I've some understanding problems regarding the performance vector of a decision tree.
I've a training data set with 16 records, which are categorized negative or positive.
I created a process and rapidminer created a new decision tree, which classifies each record correctly. ( I even checked every record manually by myself.)
Now I'd like the system to check the performance, so i added a "nominal cross validation".
Then the system reproduces the same decision, but the performance vector of this tree says, that both recall and precision are not 100%.
What's the reason for it?
I've checked the dataset manually and the decision tree seems to be allright for that specific dataset. But If i used this validation function, it says it's not?
I'dont understand this atm.
Would you be so nice and try to explain it to me?