I'm working on a multi-class text classification project.
My goal is simple: given a "bug", I'd like to predict to which final group owner it will be assigned to.
I was able to achieve ~15000 samples of bugs with a relevant status (i.e. a status I can implicitly deduce the label ==> group owner from)
My feature list:
- Comments <== can I use it at all (there's a good chance it won't be available during prediction stage)?
- Errors (being extracted via regex from the above features)
- Panics (being extracted via regex from the above features)
- Has images (boolean field) ==> ('yes' or 'no')
- Involved groups ==> Not sure I can use it as it being derived from the comments
- Committer groups
- Reporter group
- Assignee group
My labels is the final group name.
I've 6 different classes/labels
At the moment I'm getting score of ~80%
I guess my questions are:
- Can I use features that are being contained/derived from a different ones (e.g. errors being derived from comments/description)?
- How should I deal with missing features? For example Panics and Errors and not always exist, should I omit this feature? should I replace it with boolean feature such as (has errors - yes/no)?
Committer groupsI'm using the existing values to predict the new ones and add them to the dataframe, but while I can somehow understand how it can be done for such feature, I cannot see it being done to others.