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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

✨ 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

🚀 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

📋 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

📝 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"]
  }
}

📚 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

🤝 Contributing

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

📄 License

This project is licensed under the MIT License.

Metadata

Release files for mlcli-toolkit 0.3.1

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