Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to "tune hyperparameters" or "optimize model". Trigger with relevant phrases based on skill purpose.
5.8
Rating
0
Installs
Machine Learning
Category
This skill provides a solid foundation for hyperparameter tuning with clear examples and workflow explanation. The description adequately covers what the skill does (grid/random/Bayesian search optimization), and the examples demonstrate practical applications with specific models and datasets. The task knowledge is sufficient, outlining the four-step process and best practices. However, the structure could be more concise - sections like 'Prerequisites', 'Instructions', and 'Error Handling' are generic boilerplate that add clutter without specific details. The novelty is moderate: while hyperparameter tuning is valuable, modern CLI agents with code execution can reasonably accomplish this task with library documentation, though the skill does provide convenience and potentially better default patterns. Overall, a useful skill that would benefit from trimming generic sections and adding more specific technical details about parameter ranges or optimization strategies.
jeremylongshore
Skill Author
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