# Model Hyperparameter Tuning

A skill for tuning model hyperparameters: grid search, random search, Bayesian optimization with Optuna. Useful once baseline settings stop improving.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 494 installs · ★ 371
- Author: aj-geddes
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/aj-geddes/useful-ai-prompts/tree/HEAD/skills/model-hyperparameter-tuning/SKILL.md
- Updated: 2026-03-04
- HTML: https://affpapa.org/en/nevibecode/skills/model-hyperparameter-tuning

## What it does

The skill walks through a systematic search for the best combination of model configuration parameters, scored on validation data. It covers the methods: exhaustive grid search, random sampling from the parameter space, Bayesian optimization, Hyperband, evolutionary algorithms and population-based training.

Hyperparameters are broken down by model type. Tree models get `max_depth`, `min_samples_split` and `learning_rate`; neural networks get `learning_rate`, `batch_size`, `num_layers` and `dropout`; SVM gets `C`, `kernel` and `gamma`. The Python examples lean on `scikit-learn` (`GridSearchCV`, `RandomizedSearchCV`), `optuna` with `TPESampler`, and `torch` with the `Adam` optimizer. So set up the environment first — the examples won't run without those libraries.

It helps when you want to compare configurations under one method instead of hand-picking values. One caveat: grid search grows with every parameter you add, so on a wide space it burns through your time budget fast. Random search or Optuna is the better call there.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill model-hyperparameter-tuning -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/model-hyperparameter-tuning/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/model-hyperparameter-tuning/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill model-hyperparameter-tuning -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/model-hyperparameter-tuning/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill model-hyperparameter-tuning -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/model-hyperparameter-tuning/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install aj-geddes/useful-ai-prompts model-hyperparameter-tuning
```

2. **Manually: put the files in place**
- File: `.github/skills/model-hyperparameter-tuning/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill model-hyperparameter-tuning -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/aj-geddes/useful-ai-prompts --path skills/model-hyperparameter-tuning
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/model-hyperparameter-tuning/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill model-hyperparameter-tuning -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/aj-geddes/useful-ai-prompts/tree/HEAD/skills/model-hyperparameter-tuning/SKILL.md

## When to use it
- compare model configurations under one method
- pick learning rate and dropout for a neural net
- find the optimum for gradient boosting without manual sweeps
- decide between grid search and Bayesian optimization
- measure how parameters move the validation metric

## Example prompts
- Tune RandomForest hyperparameters with GridSearchCV on accuracy
- Set up an Optuna study with TPESampler to find the best model params
- Compare learning rates for a torch network and show which one drops loss faster

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d2dfd1ee27bc88fe.

BEGIN THIRD-PARTY FILE d2dfd1ee27bc88fe
````text
---
name: Model Hyperparameter Tuning
description: Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt
---

# 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

## Tuning Methods

- **Grid Search**: Exhaustive search over parameter grid
- **Random Search**: Random sampling from parameter space
- **Bayesian Optimization**: Probabilistic model-based search
- **Hyperband**: Multi-fidelity optimization
- **Evolutionary Algorithms**: Genetic algorithm based search
- **Population-based Training**: Distributed parameter optimization

## Hyperparameters by Model Type

- **Tree Models**: max_depth, min_samples_split, learning_rate
- **Neural Networks**: learning_rate, batch_size, num_layers, dropout
- **SVM**: C, kernel, gamma
- **Ensemble**: n_estimators, max_features, min_samples_leaf

## Python Implementation

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
import optuna
from optuna.samplers import TPESampler
import torch
import torch.nn as nn
from torch.optim import Adam
import time

# Create dataset
X, y = make_classification(n_samples=2000, n_features=50, n_informative=30,
                          n_redundant=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
````
END THIRD-PARTY FILE d2dfd1ee27bc88fe

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