Hyperparameter optimization is an important part of modern statistical and machine learning (ML) analysis pipelines as hyperparameters can have a strong impact of the performance of a model. There are several methods to find the optimal set of hyperparameters, from exhaustively searching a uniformly spaced grid of choices or randomly selected from a predefined search space, evolutionary and population algorithms to active learning approaches such as Bayesian optimization. No 'ground truth' data exist for MSMs used for the analysis of protein MD trajectories, so the accuracy of the eigenvectors cannot be judged absolutely. However, a family of variational scores exist which provide a means to compare the relative accuracy of MSMs and thus allow hyperparameter optimization to be performed. The first score to be developed was the cross-validated generalized matrix Rayleigh quotient, GRMQ, which pertains to reversible MSMs; while the variational approach to Markov processes (VAMP) scores extended these ideas to both reversible, non-reversible and non-stationary models. These scores measure how well the eigenvectors of the transition matrix (singular vectors in the case of non-reversible models) approximate the 'true' eigenvectors in a variational sense i.e., the higher the score, the better the approximation. Thus, optimization of eigenvectors can proceed without the need for a 'ground truth' to compare to.