Generalization and recall I have some questions regarding generalization and recall:


*

*It usually hear that a good classifier should "generalize" from the training data. Is that true? 

*I feel(?) that a "general" classifier should improve recall, however a "specific" classifier should have more precision, is this true? 

*If we think of a classifier that has to be learnt from a domain D1 and then used over a different domain D2, then a "generalized" classifier is preferred. In which other situations a "generalized" classifier is preferred?  


Thanks in advance and sorry for the vague questions...
 A: "Generalization" refers to the ability of a classifier to correctly classify instances that it has not yet seen as part of its training and is always a desirable feature. The opposite of generalization would be over-fitting, which you always want to avoid (Although a classifier might also not generalize well if the training domain is different from the domain it will finally be applied to, which might not be due to over-fitting in the strictest sense).
The terms recall and precision have very specific meanings, while your use of "general" and "specific" are not standard terminology I don't think. For binary classifiers, it is customary to refer to one class of instances as "positive" and the other as "negative". For example, if you are trying to build a classifier that recognizes pictures with faces in them, then you might define the class of all pictures that contain faces as the positive class. The term recall is then defined as the proportion of all positive instances that were classified correctly, while precision is the proportion of all instances classified as positive that were actually positive.
Here is an example. Lets say of $40$ pictures, $30$ contain faces. If a classifier classifies $25$ pictures as positive, of which $20$ actually do contain faces, then the recall is $\frac{20}{30}=2/3$ while precision is $\frac{20}{25}=0.8$.
A: Answering your 3rd question. Your training data should be as representative of the domain you are trying to learn as possible. The learned classifier should then be used in this same domain.
If you want to make use of the things you have learned in one domain and you think that this might be applied in another but somehow related domain then you are talking about 'transfer learning'. For example, a table classifier might have reusable parts if you want to learn a chair classifier.
