One very important issue when fitting a model is how well the newly-created model will behave when applied to new data. To address this issue, the data set can be divided into multiple partitions: a training partition used to create the model, a validation partition to test the performance of the model, and a third test partition. Partitioning is performed randomly, to protect against a biased partition, according to proportions specified by the user, or according to rules concerning the data set type. For example, when creating a time series forecast, data is partitioned by chronological order.