tuning-hyperparameters

Automatically searches for optimal hyperparameter configurations to improve model performance.

• Fine-tunes ML models by optimizing hyperparameters.
• Uses grid, random, or Bayesian search strategies.
• Leverages libraries like scikit-learn and Optuna.
• Identifies best hyperparameter settings for performance.

• Analyzes user requests to extract model, hyperparameters, and strategy.
• Generates and executes Python code for hyperparameter search.
• Reports best configuration and performance metrics.
• Supports multiple search strategies (grid, random, Bayesian).
• Uses cross-validation for robust evaluation.
• Integrates with other ML and code plugins.
• Improves model accuracy and performance metrics.

1. 1.State the model, hyperparameters, and search strategy in the request.
2. 2.Specify the dataset and evaluation metric (e.g., accuracy, RMSE).
3. 3.The skill generates and runs code to perform the search and reports results.