Hyperparameter Search in Machine Learning

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.

# Machine Learning and Data Classification # Computer Science # Stochastic Gradient Optimization Techniques # Advanced Bandit Algorithms Research # Decision Sciences

Marc Claesen, Bart De Moor (2015). "Hyperparameter Search in Machine Learning". arXiv (Cornell University). https://doi.org/10.48550/arxiv.1502.02127