Its interesting to note down the different flavors of multi-class classification.
- Basic multiclass classification.
- Here we have a fixed number of labels (K) and want to drop inputs into one of those K buckets.
- Basic multiclass classification with weighted examples.
- Extension of the basic multi-class, where some examples have more weight than others
- Cost-sensitive multiclass.
- Here, instead of just having one correct label (and all others incorrect), you can have different costs for each of the K different labels.
- Label-dependent features
- This is for the case where we know that we can put in additional features that depend on the label
- This is the flavor used in ‘action-dependent features’ mode of VW.