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MLCLI - Machine Learning Command Line Interface

A powerful, modular CLI tool for training, evaluating, and tracking ML/DL models

📖 Documentation • 📦 PyPI • 📚 Full Docs

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✨ Features

• ML Models: Logistic Regression, SVM, Random Forest, XGBoost
• DL Models: TensorFlow DNN, CNN, RNN/LSTM/GRU
• Hyperparameter Tuning: Grid Search, Random Search, Bayesian (Optuna)
• Model Explainability: SHAP & LIME
• Preprocessing: Scalers, Normalizers, Encoders, Feature Selection
• Experiment Tracking: Built-in tracker with JSON storage
• Interactive TUI: Terminal-based user interface

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🚀 Quick Start

Install

pip install mlcli-toolkit

Verify

mlcli --help

Train a Model

mlcli train --config configs/rf_config.json

Launch Interactive UI

mlcli ui

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📋 Commands

| Command | Description | |---------|-------------| | mlcli list-models | List available model trainers | | mlcli train -c <config> | Train a model | | mlcli eval -m <model> -d <data> -t <type> | Evaluate a model | | mlcli tune -c <config> -m <method> | Hyperparameter tuning | | mlcli explain -m <model> -d <data> -e <method> | Model explainability | | mlcli preprocess -d <data> -o <output> -m <method> | Preprocess data | | mlcli list-runs | List experiment runs | | mlcli ui | Launch interactive TUI |

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📝 Configuration Example

{
  "model": {
    "type": "random_forest",
    "params": {
      "n_estimators": 100,
      "max_depth": null,
      "random_state": 42
    }
  },
  "dataset": {
    "path": "data/train.csv",
    "type": "csv",
    "target_column": "target"
  },
  "training": {
    "test_size": 0.2,
    "random_state": 42
  },
  "output": {
    "model_dir": "artifacts",
    "save_formats": ["pickle", "joblib"]
  }
}

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📚 Documentation

For complete documentation including:

• All configuration options
• Hyperparameter tuning guides
• Model explainability (SHAP/LIME)
• Data preprocessing pipeline
• Extending MLCLI with custom trainers
• Troubleshooting

See docs/DOCUMENTATION.md

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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📄 License

This project is licensed under the MIT License.