📊 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

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📌 Installation

pip install GridSearchHelper

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🚀 Quick Start

Import and Initialize

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}')

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⚙️ Supported Models

• RandomForestClassifier 🌲
• GradientBoostingClassifier 🔥
• SVC 🛡️
• LogisticRegression 📊
• Ridge 📏
• Many more...

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🔧 Configuration

To add custom hyperparameters, simply pass them as a dictionary:

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)

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📜 License

MIT License © 2025 Abdulla Alimov

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🤝 Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

🌟 Star this repo if you find it useful!