# How to handle unseen class feature in test dataset

feature   Definition    Key
survival    Survival    0 = No, 1 = Yes
pclass   Ticket class   1 = 1st, 2 = 2nd, 3 = 3rd
sex       Sex           M/F
Age     Age in years


feature notes given below

pclass: A proxy for socio-economic status (SES)
1st = Upper
2nd = Middle
3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5


Suppose I have dataset same as mentioned above.

How to handle a case where I can have a unseen value for a class. For example : In our dataset we have 3 different value for pclass. But how to handle a case where pclass value may have 4rth class say "elite" which did not appear in training dataset but appeared in the test dataset

• I want to remind about a technique where you re-use a pre trained CNN to continue generalizing on another dataset. For example, you train on hand-written digits from MNIST, which has a ton of examples, and then apply it to your personal (much smaller) data-set of hand-written Greek characters. You can do this because they are sufficiently similar to the digits dataset. – Kari Mar 5 '18 at 23:34