What does out-of-sample testing evaluate?

Out-of-sample testing evaluates the predictive performance of a model on new, unseen data. This approach is crucial in determining how well a model generalizes beyond the dataset it was trained on. By using a separate portion of data that was not included during the training phase, out-of-sample testing helps to assess the model's accuracy and reliability when applied to real-world scenarios.

This method is important because a model that performs well on training data might not necessarily perform well in practical applications if it has not learned to generalize from the patterns in that data. Instead, it may have overfit the training data, capturing noise rather than underlying trends. Out-of-sample testing provides a more realistic estimate of how the model is likely to perform when confronted with new data, which is a critical aspect of building robust predictive models.