outofsampledata
Out-of-sample data refers to observations that are not used to fit a statistical model or train a machine learning algorithm. This data set is held back during model development and is used to assess how well the model generalizes to new, unseen cases. By comparison, in-sample data are those used for training and calibration. The concept is central to evaluating predictive performance beyond the data used for learning.
The primary purpose of out-of-sample evaluation is to estimate the expected performance of a model on future
Common approaches include the holdout method (splitting data into training and test sets), cross-validation (k-fold, stratified,
Practical considerations include avoiding data leakage, ensuring the out-of-sample set is representative, and being aware of
In many technical fields, out-of-sample testing is a standard step in model selection, forecasting, and risk