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. For example, classifying images of cats and dogs is supervisable because we can gather a collection of images and manually label each one as either "cat" or "dog." The resulting dataset can then be used to train a supervised learning model.

Conversely, problems that cannot be trained with labeled data are considered unsupervised. In unsupervised learning, the algorithm is given unlabeled data and must find patterns or structures within it, such as clustering similar data points together or reducing the dimensionality of the data. Reinforcement learning is another paradigm where learning occurs through trial and error, receiving rewards or penalties for actions taken in an environment, rather than explicit labels. The term "supervisable" specifically highlights the requirement of explicit guidance through labeled examples for successful training.