tuning-hyperparameters
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.
npx skills add majiayu000/claude-skill-registry --skill tuning-hyperparameters --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Hyperparameter Tuner This skill provides automated assistance for hyperparameter tuner tasks. ## Overview This skill empowers Claude to fine-tune machine learning models by automatically searching for the optimal hyperparameter configurations. It leverages different search strategies (grid, random, Bayesian) to efficiently explore the hyperparameter space and identify settings that maximize model performance. ## How It Works 1. **Analyzing Requirements**: Claude analyzes the user's request to determine the model, the hyperparameters to tune, the search strategy, and the evaluation metric. 2. **Generating Code**: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn, Optuna) to implement the specified hyperparameter search. The code includes data loading, preprocessing, model training, and evaluation. 3. **Executing Search**: The generated code is executed to perform the hyperparameter search. The plugin iterates through different hyperparameter combinations, trains the model with each combination, and evaluates its performance. 4. **Reporting Results**: Claude reports the best hyperparameter configuration found during the search, along with the correspon
- Overview
- How It Works
- When to Use This Skill
- Examples
- Example 1: Optimizing a Random Forest Model
- Example 2: Using Bayesian Optimization
- Best Practices
- Integration
- Prerequisites
- Instructions
- Output
- Error Handling
- Resources
What does the tuning-hyperparameters skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill tuning-hyperparameters --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.