Train ML Models
From the Command Line
A production-ready CLI tool for training, evaluating, and managing machine learning models with experiment tracking, hyperparameter tuning, and model explainability.
pip install mlcli-toolkit15+
ML Models
3
Tuning Methods
10+
Preprocessors
2
Explainability Tools
Everything You Need for ML Workflows
From data preprocessing to model deployment, mlcli provides a complete toolkit for machine learning practitioners.
CLI & TUI Interface
Train models from the command line or use the interactive TUI for a rich experience.
Multiple Algorithms
Support for Random Forest, XGBoost, LightGBM, CatBoost, SVM, and deep learning models.
Experiment Tracking
Track experiments, compare runs, and visualize metrics with built-in tracking.
Hyperparameter Tuning
Grid search, random search, and Bayesian optimization for optimal parameters.
Model Explainability
Understand your models with SHAP and LIME explanations and feature importance.
Data Preprocessing
Built-in preprocessing pipelines with scalers, encoders, and feature selection.
Simple Yet Powerful CLI
Get started in seconds with intuitive commands. Train models, track experiments, and tune hyperparameters with just a few keystrokes.
- Train multiple model types with one command
- Automatic experiment tracking and logging
- Built-in hyperparameter optimization
- SHAP and LIME model explanations
- Preprocessing pipelines included
# Install mlcli-toolkit
pip install mlcli-toolkit
# Train a model using config file
mlcli train --config configs/rf_config.json
# Evaluate the model
mlcli eval --model models/rf_model.pkl --data test.csv
# Track experiments
mlcli list-runs
# Tune hyperparameters
mlcli tune --config configs/tune_config.jsonQuick Links
Jump into the documentation or explore the experiment dashboard.
Ready to Get Started?
Join the community of ML practitioners using mlcli to streamline their workflows.