I am quite new to some concepts of machine learning and having hard time understanding the following.
Suppose I have a supervised classifier (random forest) trained with a dataset with several features.
Do the features in the test dataset need to have values that are somewhat similar (or closer) to the training data (or in the same domain).
For example, take training data record: <'label A', 12, 23, 3412, 65> (assume other 'label A' types are similar to this, with only +-10 difference for each feature), test data: X: <10, 21, 3000, 80> and Y: <0.12. 0.23, 34.12, 0.65>.
Out of X and Y, which has a higher chance of being classified as type 'label A'?
Please make a note of any assumptions you make.