About

Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/tuning-hyperparameters/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill tuning-hyperparameters. Copyright stays with the author (MIT).

Hyperparameter Tuner

Name: tuning-hyperparameters
Author: jeremylongshore

Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization to maximize performance.

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

Analyzing Requirements: Claude analyzes the user's request to determine the model, the hyperparameters to tune, the search strategy, and the evaluation metric.
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.
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.
Reporting Results: Claude reports the best hyperparameter configuration found during the search, along with the corresponding performance metrics. It also provides insights into the search process and potential areas for further optimization.

When to Use This Skill

This skill activates when you need to:

Optimize the performance of a machine learning model.
Automatically search for the best hyperparameter settings.
Compare different hyperparameter search strategies.
Improve model accuracy, precision, recall, or other relevant metrics.

Examples

Example 1: Optimizing a Random Forest Model

User request: "Tune hyperparameters of a Random Forest model using grid search to maximize accuracy on the iris dataset. Consider n_estimators and max_depth."

The skill will:

Generate code to perform a grid search over the specified hyperparameters (n_estimators, max_depth) of a Random Forest model using the iris dataset.
Execute the grid search and report the best hyperparameter combination and the corresponding accuracy score.

Example 2: Using Bayesian Optimization

User request: "Optimize a Gradient Boosting model using Bayesian optimization with Optuna to minimize the root mean squared error on the Boston housing dataset."

The skill will:

Generate code to perform Bayesian optimization using Optuna to find the best hyperparameters for a Gradient Boosting model on the Boston housing dataset.
Execute the optimization and report the best hyperparameter combination and the corresponding RMSE.

Best Practices

Define Search Space: Clearly define the range and type of values for each hyperparameter to be tuned.
Choose Appropriate Strategy: Select the hyperparameter search strategy (grid, random, Bayesian) based on the complexity of the hyperparameter space and the available computational resources. Bayesian optimization is generally more efficient for complex spaces.
Use Cross-Validation: Implement cross-validation to ensure the robustness of the evaluation metric and prevent overfitting.

Integration

This skill integrates seamlessly with other Claude Code plugins that involve machine learning tasks, such as data analysis, model training, and deployment. It can be used in conjunction with data visualization tools to gain insights into the impact of different hyperparameter settings on model performance.

Prerequisites

Appropriate file access permissions
Required dependencies installed

Instructions

Invoke this skill when the trigger conditions are met
Provide necessary context and parameters
Review the generated output
Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

Invalid input: Prompts for correction
Missing dependencies: Lists required components
Permission errors: Suggests remediation steps

Resources

Project documentation
Related skills and commands

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/jeremylongshore-tons-of-skills-marketplace-tuning-hyperp-456afb/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

jeremylongshore-tons-of-skills-marketplace-tuning-hyperp-456afb.ocm.jsonjson

{
  "ocm": "1",
  "id": "jeremylongshore-tons-of-skills-marketplace-tuning-hyperp-456afb",
  "kind": "skill",
  "name": "tuning-hyperparameters",
  "description": "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.",
  "publisher": "jeremylongshore",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "ai",
      "ml",
      "performance",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/jeremylongshore/tons-of-skills-marketplace",
      "path": "skills/.curated/tuning-hyperparameters/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/jeremylongshore/tons-of-skills-marketplace/blob/HEAD/skills/.curated/tuning-hyperparameters/SKILL.md",
      "key": "jeremylongshore/tons-of-skills-marketplace/skills/.curated/tuning-hyperparameters/SKILL.md"
    },
    "compatibility": "Designed for Claude Code",
    "allowed_tools": [
      "Read,",
      "Write,",
      "Edit,",
      "Grep,",
      "Glob,",
      "Bash(cmd:*)"
    ],
    "license": "MIT"
  },
  "instructions": "# Hyperparameter Tuner\n\nOptimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization to maximize performance.\n\n## Overview\n\nThis 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.\n\n## How It Works\n\n1. **Analyzing Requirements**: Claude analyzes the user's request to determine the model, the hyperparame",
  "cost": {
    "context_tokens": 1026
  }
}

Fetch it by URL: GET /api/v1/registry/jeremylongshore-tons-of-skills-marketplace-tuning-hyperp-456afb/manifest?version=1.0.0

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