Hello,
I've recently started using RapidMiner, and have found it rather intuitive to use so far. However, now, I'm stuck
!
I have created a text classification model using TextInput (BinaryOccurrences) and XValidation with LibSVMLearner (as in the example 01_TextClassificationXVal.xml). At the end of the process, I added a ModelWriter.
In order to classify unknown texts, I created a new process. In this process, I chain a TextInput with the same parameters as before, a ModelLoader, a ModelApplier, and a ClassificationPerformance.
Since I use the same text inputs for learning and testing, I would expect the same performances. However, in the learning phase, I have an accuracy of 82.58%, but in the testing phase, I get only 26.49%...
Any ideas? ???
Edit: I was using "prune_below 90%" for learning, and "prune_below -1" for testing. This gave the error message "[Warning] Kernel Model: The number of regular attributes of the given example set does not fit the number of attributes of the training example set, training: 288, application: 2836
" When I set both to the same value, I get the expected high testing accuracy. However, I don't understand why that is the case! Any explanation is still welcome
How should I go about to correctly load my model and classify an unknown text?
Edit again: Solved it for good by saving and loading the word vector used. phew! 8)
