To evaluate model performance, QSPRpred provides a class called ModelAssessor that defines a structure for performance evaluation. Given a QSPRpred model and a dataset, it will run an evaluation and return the specified metric. All scikit-learn scoring functions can be used or custom ones can be provided to the ModelAssessor. given by the provided metric. On the other hand, the CrossValAssessor can perform cross-validation on the training set. The folds are determined by the user-specified splitting method (see section 2.1.5). It will return a list of scores for each fold. Because of the flexible splitting method, this class is not limited to cross-validation, but can also perform bootstrapping, through resampling of the training set (see section 2.1.5).