Welcome to the MLCLI documentation!

• Getting Started
• Installation
• User Guide
• API Reference
• Examples
• Contributing

MLCLI is a powerful, modular command-line interface for training, evaluating, and tracking machine learning and deep learning models. It provides:

• Unified CLI for multiple ML frameworks (scikit-learn, TensorFlow, XGBoost)
• Configuration-driven training with JSON/YAML files
• Built-in experiment tracking with JSON storage
• Model explainability with SHAP and LIME
• Hyperparameter tuning with Grid, Random, and Bayesian optimization
• Interactive TUI for guided workflows

pip install mlcli-toolkit

# Train a model
mlcli train --config configs/rf_config.json

# Evaluate a model
mlcli evaluate --model models/rf_model.pkl --data data/test.csv

# Launch interactive UI
mlcli ui

docs/
├── index.md                 # This file
├── getting-started.md       # Quick start guide
├── installation.md          # Installation instructions
├── user-guide/
│   ├── index.md
│   ├── training.md
│   ├── evaluation.md
│   ├── tuning.md
│   ├── preprocessing.md
│   └── explainability.md
├── api/
│   ├── index.md
│   ├── trainers.md
│   ├── tuners.md
│   └── preprocessors.md
└── tutorials/
    ├── index.md
    └── custom-trainer.md

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