Model accuracy changes when target attribute change I have a dataset. For a target attribute for example:person, I get an accuracy which is different when I use for example:price as target attribute and so on.

Why this accuracy keep changing?
There is not reason to believe that the accuracy wouldn't change as you change the target. After all, the features that is able to explain a target distribution could be very different from the features that is able to explain another target distribution. 

It is similar to how human learn. We can't expect a human who is just trained to speak English to suddenly speak fluently in another language.