Hyperparameter tuning refers to the process of selecting the optimal hyperparameters for a machine-learning algorithm. Hyperparameters are parameters set before the learning process begins, such as learning rate, number of hidden layers, and batch size, which impact model performance but are not learned during training.
- Glossary > Letter: H
What does "Hyperparameter Tuning" mean?

Use Cases
Neural Networks::
Adjusting learning rates, batch sizes, and activation functions to optimize performance.
Support Vector Machines: :
Tuning kernel types and regularization parameters for improved accuracy.
Decision Trees:
Setting maximum depths and minimum sample splits to balance model complexity and accuracy.

Importance
Performance Optimization:
Enhances model accuracy and efficiency by finding the best hyperparameter values.
Generalization:
Improves the model's ability to perform well on new, unseen data.
Time and Resource Efficiency:
Reduces trial and error by systematically exploring hyperparameter combinations.

Analogies
Hyperparameter tuning is like adjusting the settings on a musical instrument. Just as you tune strings and adjust keys to produce the best sound, tuning hyperparameters optimizes the performance of machine learning models to achieve the best results.
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