Multiclassification - class(es) not predicted
Hi guys,
i've been playing around with RM for about 2 months now and the more i work with the more i'm impressed about functionality and flexibility. Congratulations to all of the contributors of this great software package!
One problem that i'm currently struggling with is a multinominal classification task with three label classes of which only two are predicted. The dataset consists of performance measures that are categorized by a kind of rating - lets say A, B, C where A is the top and C the lowest rating. The dataset is fairly balanced (A - 35%, B - 30%, C - 35%) - nevertheless most of the classification learners (except the DT with gini_index criterion) do not predict the B rating at all.
From the contents of the dataset i know that boundaries between A - B and B - C heavily blur, but i can't explain why no example is assigned the B class. I'm able to influence the range of B's confidence by adding/removing features but it's never getting greater than any A or C confidence and hence is not taken as the prediction. What do you think? Is it possible that a single class simply has no predictive power? I read some posts about binominal classifications where only one class was predicted but these don't seem to relate to this problem.
Thank you for your time!
i've been playing around with RM for about 2 months now and the more i work with the more i'm impressed about functionality and flexibility. Congratulations to all of the contributors of this great software package!
One problem that i'm currently struggling with is a multinominal classification task with three label classes of which only two are predicted. The dataset consists of performance measures that are categorized by a kind of rating - lets say A, B, C where A is the top and C the lowest rating. The dataset is fairly balanced (A - 35%, B - 30%, C - 35%) - nevertheless most of the classification learners (except the DT with gini_index criterion) do not predict the B rating at all.
From the contents of the dataset i know that boundaries between A - B and B - C heavily blur, but i can't explain why no example is assigned the B class. I'm able to influence the range of B's confidence by adding/removing features but it's never getting greater than any A or C confidence and hence is not taken as the prediction. What do you think? Is it possible that a single class simply has no predictive power? I read some posts about binominal classifications where only one class was predicted but these don't seem to relate to this problem.
Thank you for your time!