  It is typical for a machine learning system to have numerous hyperparameters
that affect its learning rate and prediction quality. Finding a good
combination of the hyperparameters is, however, a challenging job. This is
mainly because evaluation of each combination is extremely expensive
computationally; indeed, training a machine learning system on real data with
just a single combination of hyperparameters usually takes hours or even days.
In this paper, we address this challenge by trying to predict the performance
of the machine learning system with a given combination of hyperparameters
without completing the expensive learning process. Instead, we terminate the
training process at an early stage, collect the model performance data and use
it to predict which of the combinations of hyperparameters is most promising.
Our preliminary experiments show that such a prediction improves the
performance of the commonly used random search approach.
