In the machine learning, at least for some classes of problems, a predictive model is built based on training the dataset where the outcome is known, and the same model is then used to predict the outcome from actual input data. The model may be further refined when larger numbers of data with known outcomes are available, or in a continual fashion, when a 'training' occurs on an incoming data for which the current model has failed to predict the correct outcome. I.e. the data point that has been 'trained' enlarges the training dataset thus occasioning the model to be rebuilt considering one more datum points with known outcomes, than was previously available.