AI GlossaryHyperparameter TuningENTRY — Machine Learning
[Machine Learning]
Hyperparameter Tuning.
The process of finding the optimal hyperparameter values for a machine learning model.
In-depth explanation
01Hyperparameter tuning searches the space of possible hyperparameter combinations to find the best configuration. Methods include grid search (exhaustive search), random search (random sampling), and Bayesian optimization (intelligent search based on past results). Automated tools like Optuna and Ray Tune help streamline this process.
Examples
02EX. 01
Finding optimal learning rate
EX. 02
Selecting number of hidden layers
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03Ensemble LearningCombining multiple models to produce better predictions than any single model.04FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.05Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.06Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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