MLTuneX 0.1.5

A package for machine learning tuning and optimization.

🤖 MLTuneX - AutoML Framework for Model Training and Hyperparameter Tuning

MLTuneX is a powerful and extensible AutoML library designed to make machine learning model training and hyperparameter tuning easy, customizable, and scalable.

🚀 With support for preprocessed data (currently), the library can:

• Train multiple models
• Evaluate their performance
• Tune top models using Optuna and OpenAI GPT-based guidance
• Save the best-performing model

⚙️ Currently supports:

• Model Library: scikit-learn
• Tuning Framework: Optuna

🧪 Upcoming support:

• Grid Search
• Random Search
• Ray Tune
• OpenAI-based advanced tuning agents

---

🤖 Supported LLMs for Tuning

MLTuneX uses large language models to guide tuning strategies. You can specify the provider and model using the model_provider_model_name argument:

✅ OpenAI:

• OpenAI:gpt-4o

✅ Groq:

• Groq:deepseek-r1-distill-llama-70b
• Groq:qwen/qwen3-32b

ℹ️ Additional model support will be added in future updates. Contributions are welcome!

---

⚠️ NOTE: As of now, only preprocessed data is supported. You must provide a dataset that is already cleaned and encoded. Automated raw data handling is planned in upcoming versions.

---

📦 Installation

Install the package directly using pip:

pip install --no-cache-dir MLTuneX

export OPENAI_API_KEY="your-openai-api-key-here"
export GROQ_API_KEY="your-groq-api-key"

from mltunex.main import MLTuneX

mltunex = MLTuneX(
    data="/path/to/your/preprocessed_data.csv",  # Must be a cleaned CSV or pandas DataFrame
    target_column="your_target_column",          # Specify the target column
    task_type="regression",                      # Choose between "regression" or "classification"
    model_provider_model_name = "OpenAI:gpt-4o"
)

mltunex.run(
    result_csv_path="/path/to/save/csv",         # Directory to store evaluation results
    model_dir_path="/path/to/save/models",       # Directory to save the best model
    tune_models="yes"                            # "yes" to enable hyperparameter tuning
)