# Training binary classifier on only one data point ( Theoritical question)

Say, I'm training a binary classifier to classify Dog vs Cat. Now, say I train my model only on one imagee ( cat). Now I mirror this cat image that I used to train my model. Now on the mirror image I want to make predciton.

Question : What will my model predict ? does it predict cat, or will it randomly predict either cat or dog

I think it will predict cat beacuse the parameters have been trained only for cat, so irrespective of any input data, the parameters will support prediction for cat. Am I correct ? is my reasoning correct?

• It depends on the type of model but if you have only 1 class in the training set, the model will probably assume that 100% of the instances are "cat" since it doesn't know anything else. Actually it's likely that the training will just fail with an error. Dec 10 '20 at 12:44

• @blacksheep369 It feels like you have CNNs in mind when you talk about "learning features of a cat". Conv layers of a CNN learn features but they do not know whether these features belong to a cat or a dog. It is the job of the last FC layers to learn the decision curve that separates the features into 2 categories. I denoted these features schematically $w_1$ and $w_2$. You may assume that they are outputs of your conv layers. Your net learned that certain $w_1$ and $w_2$ is a cat and moved the separating line to include that point. But it has too little info where exactly to put this line. Dec 11 '20 at 1:13