  For many machine learning models, a choice of hyperparameters is a crucial
step towards achieving high performance. Prevalent meta-learning approaches
focus on obtaining good hyperparameters configurations with a limited
computational budget for a completely new task based on the results obtained
from the prior tasks. This paper proposes a new formulation of the tuning
problem, called consolidated learning, more suited to practical challenges
faced by model developers, in which a large number of predictive models are
created on similar data sets. In such settings, we are interested in the total
optimization time rather than tuning for a single task. We show that a
carefully selected static portfolio of hyperparameters yields good results for
anytime optimization, maintaining ease of use and implementation. Moreover, we
point out how to construct such a portfolio for specific domains. The
improvement in the optimization is possible due to more efficient transfer of
hyperparameter configurations between similar tasks. We demonstrate the
effectiveness of this approach through an empirical study for XGBoost algorithm
and the collection of predictive tasks extracted from the MIMIC-IV medical
database; however, consolidated learning is applicable in many others fields.
