outofsamplejaksoihin
outofsamplejaksoihin is a Finnish term that translates to "out-of-sample periods" in English. It refers to the time intervals or data points that are not used during the training or development of a statistical model, machine learning algorithm, or forecasting system. The primary purpose of out-of-sample periods is to provide an unbiased evaluation of the model's performance and its ability to generalize to new, unseen data.
When building predictive models, it is crucial to split the available data into at least two sets:
The evaluation conducted on out-of-sample periods is vital for determining the real-world applicability and reliability of