Abstract: A method that includes receiving user input associated with identifying a threshold point associated with a classification task, identifying, a machine learning model, in the first set of visual data, a machine placement candidate point associated with identifying the threshold point, and identifying, based on the machine placement candidate point, a set of baseline confidence values via a baseline uncertainty model. The method includes training the machine learning model based on a determined state space by identifying, subsequent sets of visual data additional threshold points, receiving user feedback indicating an accuracy, comparing the baseline confidence values with locations associated with the additional threshold points, generating reward values based on an identified amount of error, and configuring the machine learning model based on the reward values.

Abstract: A method of reducing uncertainty in a machine learning model that may include providing a first set of visual data, and receiving user input associated with identifying a threshold point in the first set of visual data. The method may include identifying via a machine learning model, in the first set of visual data, a machine placement candidate point associated with identifying the threshold point, and training, by the processing device, baseline and cognitive uncertainty models. The method may include identifying via the trained baseline uncertainty model, one or more confidence values associated with a machine placement candidate point for a visual feature in a second set of visual data, and identifying via the trained cognitive uncertainty model, one or more confidence values associated with a machine placement candidate point for a visual feature in a second set of visual data, the visual feature being associated with the classification task.