"The division of the input data into training, testing and validation sets is crucial in the creation of robust machine learning algorithms. Firstly, machine learning algorithms require a training set to be trained on. Each iteration, it calculates the difference between the predicted and actual outcomes and refines the algorithms weightings accordingly to reduce this difference. The algorithm produced is tailored specifically to the training data set. To assess the generalisability of the final algorithm and its learnt parameters, it is tested on a separate testing data set."
"The three data sets should be randomly divided at the commencement of the project with the ratio dependent on the specific project and total data size. A not uncommon starting point is a ratio of 60:20:20 for training, validation and testing sets, respectively."