Hyperparameter search space
A hyperparameter search space is the set of candidate values, ranges, or combinations considered when tuning a model or learning procedure.
Designing Deep Learning Systems: A guide for software engineers
The set of allowable values or value ranges from which hyperparameters may be selected during optimization.
- How it is used
- Defines allowable trial configurations for HPO requests, so algorithms can sample values and optimize a training objective.
- How deep it goes
- Explains discrete, continuous, categorical, and conditional spaces through grid, random, and tool-defined configurations without mathematical derivations.
- Examples
- Uses learning rate and batch size grids, conditional classifier settings, Optuna suggestions, and Ray Tune learning-rate and momentum configurations.
- What you can do after
- Apply it via Hyperopt, Optuna, or Ray Tune by defining candidate values, ranges, and conditional parameters for model trials.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A hyperparameter search space is the set of candidate values and constraints from which model configurations are selected during optimization.
- How it is used
- Guides systematic model tuning by restricting candidate settings before cross-validation and search.
- How deep it goes
- Explains candidate values, boundaries, conditional parameters, and grid-size trade-offs but stops short of full search-algorithm theory.
- Examples
- Uses logistic-regression grids and an Optuna XGBoost study with categorical choices, numeric ranges, conditional settings, and optimization diagnostics.
- What you can do after
- Apply it via Scikit-learn or Optuna to define candidate settings, evaluate configurations, and select a lower-error model.
Related concepts
- hyperparameter tuningused by · Hyperparameter tuning is the process of selecting or adjusting configurable model or procedure settings and evaluating them against a chosen objective.
- hyperparameter gridpart of · A hyperparameter grid is a structured set of candidate hyperparameter values whose combinations define configurations for systematic evaluation.
- Optunaused by · Optuna is an open-source Python framework for automated hyperparameter optimization through repeated trials that explore parameter configurations.
- grid searchused by · Grid search is a search method that evaluates configurations formed from specified candidate values, typically every combination in their Cartesian product.
- search spacekind of · A search space is the set of possible states, choices, configurations, or candidate solutions that may be considered when exploring or solving a problem.
- hyperparameterhas part · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
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