Supervisable
Supervisable refers to a property of a learning problem or model that allows it to be trained using labeled data. In supervised learning, the goal is to learn a mapping from input features to output labels. This mapping is learned by presenting the algorithm with a dataset where each input example is paired with its corresponding correct output. The algorithm then adjusts its internal parameters to minimize the difference between its predictions and the actual labels.
A problem is considered supervisable if there exists a way to generate or obtain such labeled data.
Conversely, problems that cannot be trained with labeled data are considered unsupervised. In unsupervised learning, the