  The most important part of model selection and hyperparameter tuning is the
evaluation of model performance. The most popular measures, such as AUC, F1,
ACC for binary classification, or RMSE, MAD for regression, or cross-entropy
for multilabel classification share two common weaknesses. First is, that they
are not on an interval scale. It means that the difference in performance for
the two models has no direct interpretation. It makes no sense to compare such
differences between datasets. Second is, that for k-fold cross-validation, the
model performance is in most cases calculated as an average performance from
particular folds, which neglects the information how stable is the performance
for different folds.
  In this talk, we introduce a new EPP rating system for predictive models. We
also demonstrate numerous advantages for this system, First, differences in EPP
scores have probabilistic interpretation. Based on it we can assess the
probability that one model will achieve better performance than another.
Second, EPP scores can be directly compared between datasets. Third, they can
be used for navigated hyperparameter tuning and model selection. Forth, we can
create embeddings for datasets based on EPP scores.
