Is it possible to have a default class in multi class classification? In the general text classification problem, training a machine learning model to detect if a text belongs to one of N number of classes always yields a value in N. Even if the text that was passed to the model is very far outside any of the N classes (like say you trained it on categories for movie genres, but someone passed a recipe to the classifier), it will always choose one of N as the output even though the text passed to it doesn't belong to any of the classes.  

Is there a method to have an additional class for 'unknown' so that if the model output is one of the N types but matches it with very low probability then assign it to the default or unknown class? 

EDIT: we are using LinearSVC from sci kit learn
Depends on the model but this would be a post-processing move.

E.g. in a recent problem I trained a random forest classifier and then classified any data point where the probability of the likeliest class was less than 50% as unknown.