Finding the right hyperparameters for a model, can be a challenging task. As with previously discussed components, QSPRpred provides a flexible base class HyperparameterOptimization. Hyperparameter optimization requires specification of the search space; which model parameters to tune and for which values or within which bounds. A template of how to provide the search space file can be found in the documentation . Furthermore, a ModelAssessor needs to be specified, that determines how a set of hyperparameters will be evaluated. If the ModelAssessor returns multiple scores, e.g. in the case of cross-validation a score for each fold is returned, the score will be aggregated by a user-specified function.