1. A computer-implemented method comprising: receiving at least one user action from a user input device, the at least one user action including a plurality of actions performed on an object; determining a type of object that includes the object on which the at least one user action was performed; determining a feature vector of the at least one user action and the object type on which the at least one user action was performed; using the at least one user action and the feature vector to create a set of training data, the training data used to predict future user actions for objects of the type and to determine an accuracy of the predicted future user actions; and selecting a sample size of users from which to receive additional user actions for the object, the sample size selected responsive to the determined accuracy of the predicted future user actions, the sample size reflecting an extent to which the predicted future user actions substitute for the at least one user action from the user input device, substitution beginning at least in response to the determined accuracy of the predicted user actions being at or near the accuracy of the at least one user action and the extent of substitution increasing over time as the determined accuracy of predicted user actions improves relative to the accuracy of the at least one user action.