Initially, all active machine learning algorithms explore the entire design space which can result in counter-intuitive or trivial queries. Consequently, the experimentalist loses confidence in the machine learning tool. The initial selection of experiments does not rely on any preliminary or physical knowledge within the machine learning models. Therefore, this issue is related to both the human bias and the perception of these algorithms by their users, and to the absence of preliminary knowledge within these models. The incorporation of preliminary knowledge into active machine learning models will be discussed in section 4.1.