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GlossaryMachine LearningHyperparameter Tuning

Hyperparameter Tuning

[ˈhaɪpərpəˌræmɪtər ˈtuːnɪŋ]
Machine Learning
Last updated: December 9, 2024

Definition

Optimizing settings that govern the learning process.

Detailed Explanation

Hyperparameter Tuning is the process of selecting the best hyperparameters (settings) for a learning algorithm. Techniques like grid search and random search are used to find the optimal combination that improves model performance.

Use Cases

Improving model accuracy optimizing training balancing bias and variance model development.

Related Terms

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Predicting continuous numerical values from input data.

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Stacking

Combining multiple models using a meta-model for predictions.

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Random Forests

An ensemble learning method that combines multiple decision trees to improve prediction accuracy.

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