Glossary Library

Technical GlossaryMachine Learning

Successive Halving

An optimization approach that evaluates model candidates in stages, eliminates weak ones, and allocates resources to the strongest candidates.

Successive Halving is used to reduce the cost of full hyperparameter evaluation. Many candidates are started with a small budget, weaker ones are removed, and the remaining strong candidates continue with more resources. This can save significant time for models with long training cycles. However, one must consider carefully whether early-stage performance reliably predicts final performance.

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Optimization

The process of systematically improving model parameters according to an objective function in order to increase performance.

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Gradient Descent

A fundamental optimization method that updates parameters in the opposite direction of the gradient to minimize a loss function.

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Stochastic Gradient Descent

An optimization approach that updates parameters using single examples or small subsets instead of the full dataset at each step.