Need help on removing classifier model skew
Hi,
To provide a bried background to my exercise,
My objective is to create a SVM Classifier model which would classify customer feedback(attribute) into one of the various categories(Label).For this am trying to generate features from feedback verbatims which I then pass as attributes to the model.
The issue that am facing is, it could be observed from the classification errors of the model that the model is highly skewed towards the categories where the number of occurences was high(for highest frequency segment: class precision = low but class recall = high), i.e, the categories with lower frequencies were also being predicted as the ones with highest frequency. I have tried weighting the lower frequency segments suitably to remove differences in the occurences, but the errors are only getting magnified. Please let me know if there is any other way in which this can be controlled.
Many thanks in advance,
Ram
To provide a bried background to my exercise,
My objective is to create a SVM Classifier model which would classify customer feedback(attribute) into one of the various categories(Label).For this am trying to generate features from feedback verbatims which I then pass as attributes to the model.
The issue that am facing is, it could be observed from the classification errors of the model that the model is highly skewed towards the categories where the number of occurences was high(for highest frequency segment: class precision = low but class recall = high), i.e, the categories with lower frequencies were also being predicted as the ones with highest frequency. I have tried weighting the lower frequency segments suitably to remove differences in the occurences, but the errors are only getting magnified. Please let me know if there is any other way in which this can be controlled.
Many thanks in advance,
Ram