# 📊 GridSearchHelper: Advanced Hyperparameter Tuning Library
Welcome to **GridSearchHelper**, a powerful and flexible hyperparameter tuning library designed to make model optimization effortless! 🚀
## ✨ Features
- 🔄 **Automated Hyperparameter Grid Generation** for supported models
- 📈 **Seamless Integration** with Scikit-Learn's GridSearchCV
- ⚡ **Supports Classification & Regression Models**
- 🛠️ **Customizable Parameter Grids**
- 🎯 **Easy-to-Use API**
---
## 📌 Installation
```bash
pip install GridSearchHelper
```
---
## 🚀 Quick Start
### Import and Initialize
```python
from GridSearchHelper import perform_grid_search
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_diabetes
# Load dataset
data = load_diabetes()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Run Hyperparameter Tuning
best_params, best_score, grid_search = perform_grid_search(
model_name='Ridge',
X_train=X_train_scaled,
y_train=y_train,
cv_folds=5,
scoring='neg_mean_squared_error'
)
print(f'Best Parameters: {best_params}')
```
---
## ⚙️ Supported Models
- RandomForestClassifier 🌲
- GradientBoostingClassifier 🔥
- SVC 🛡️
- LogisticRegression 📊
- Ridge 📏
- Many more...
---
## 🔧 Configuration
To add custom hyperparameters, simply pass them as a dictionary:
```python
custom_params = {
'alpha': [0.01, 0.1, 1, 10],
'solver': ['auto', 'svd', 'cholesky']
}
perform_grid_search('Ridge', X_train_scaled, y_train, additional_params=custom_params)
```
---
## 📜 License
MIT License © 2025 Abdulla Alimov
---
## 🤝 Contributing
Contributions are welcome! Feel free to submit issues or pull requests.
🌟 **Star this repo if you find it useful!**
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"description": "# \ud83d\udcca GridSearchHelper: Advanced Hyperparameter Tuning Library\n\nWelcome to **GridSearchHelper**, a powerful and flexible hyperparameter tuning library designed to make model optimization effortless! \ud83d\ude80\n\n## \u2728 Features\n\n- \ud83d\udd04 **Automated Hyperparameter Grid Generation** for supported models\n- \ud83d\udcc8 **Seamless Integration** with Scikit-Learn's GridSearchCV\n- \u26a1 **Supports Classification & Regression Models**\n- \ud83d\udee0\ufe0f **Customizable Parameter Grids**\n- \ud83c\udfaf **Easy-to-Use API**\n\n---\n\n## \ud83d\udccc Installation\n\n```bash\npip install GridSearchHelper\n```\n\n---\n\n## \ud83d\ude80 Quick Start\n\n### Import and Initialize\n\n```python\nfrom GridSearchHelper import perform_grid_search\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.datasets import load_diabetes\n\n# Load dataset\ndata = load_diabetes()\nX, y = data.data, data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Scale features\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n\n# Run Hyperparameter Tuning\nbest_params, best_score, grid_search = perform_grid_search(\n model_name='Ridge',\n X_train=X_train_scaled,\n y_train=y_train,\n cv_folds=5,\n scoring='neg_mean_squared_error'\n)\n\nprint(f'Best Parameters: {best_params}')\n```\n\n---\n\n## \u2699\ufe0f Supported Models\n\n- RandomForestClassifier \ud83c\udf32\n- GradientBoostingClassifier \ud83d\udd25\n- SVC \ud83d\udee1\ufe0f\n- LogisticRegression \ud83d\udcca\n- Ridge \ud83d\udccf\n- Many more...\n\n---\n\n## \ud83d\udd27 Configuration\n\nTo add custom hyperparameters, simply pass them as a dictionary:\n\n```python\ncustom_params = {\n 'alpha': [0.01, 0.1, 1, 10],\n 'solver': ['auto', 'svd', 'cholesky']\n}\nperform_grid_search('Ridge', X_train_scaled, y_train, additional_params=custom_params)\n```\n\n---\n\n## \ud83d\udcdc License\n\nMIT License \u00a9 2025 Abdulla Alimov\n\n---\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Feel free to submit issues or pull requests.\n\n\ud83c\udf1f **Star this repo if you find it useful!**\n\n\n\n",
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