Which term describes the process of assessing how well your model generalizes to independent datasets?

The term that describes the process of assessing how well a model generalizes to independent datasets is cross-validation. Cross-validation is a statistical method used to estimate the skill of machine learning models. It involves partitioning the dataset into a training set and a validation (or test) set multiple times. By using different subsets of the data for training and testing, cross-validation provides a more accurate measure of the model's ability to perform well on unseen data, thereby helping to assess its generalization capabilities.

This method helps in identifying overfitting, where a model performs well on the training data but poorly on new, independent data, allowing practitioners to choose models that not only fit the training data but also predict accurately across different datasets. Consequently, cross-validation serves as a critical tool for ensuring that a model can successfully generalize rather than simply memorizing the data it was trained on.