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Model Hyperparameter Tuning

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Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt

skills/model-hyperparameter-tuning/SKILL.md

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# Model Hyperparameter Tuning

## Overview

Hyperparameter tuning is the process of systematically searching for the best combination of model configuration parameters to maximize performance on validation data.

## When to Use

- When optimizing model performance beyond baseline configurations
- When comparing different parameter combinations systematically
- When fine-tuning complex models with many hyperparameters
- When seeking the best trade-off between bias, variance, and training time
- When improving model generalization on validation and test data
- When exploring parameter spaces for neural networks, tree models, or ensemble methods
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