Hyperparameter Search in Machine Learning
This is an incremental discussion of a known bottleneck for machine learning practitioners.
The paper tackles the problem of hyperparameter selection in machine learning, emphasizing that it significantly impacts model performance and requires a disciplined search strategy, but provides no concrete results or numbers.
We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based on given data. Most common learning algorithms feature a set of hyperparameters that must be determined before training commences. The choice of hyperparameters can significantly affect the resulting model's performance, but determining good values can be complex; hence a disciplined, theoretically sound search strategy is essential.