Lazy Predict is an AutoML library that builds and compares multiple machine learning models with minimal code.
The tool addresses the problem of model selection by automatically training a broad set of baseline models across classification, regression, and time series forecasting tasks without requiring hyperparameter tuning. It trains over forty built-in models and returns ranked results so users can quickly identify which approaches work best for their data. For time series, it includes twenty or more forecasting models spanning statistical methods like ARIMA and ETS, machine learning approaches such as Random Forest and XGBoost, deep learning options including LSTM and GRU, and pretrained foundation models like TimesFM. The library handles categorical feature encoding through multiple strategies, integrates with MLflow for experiment tracking, and supports GPU acceleration across XGBoost, LightGBM, CatBoost, cuML, and deep learning models.
Lazy Predict suits teams exploring which model families might work for a new problem or those needing quick baseline comparisons without extensive tuning effort. It works well for classification and regression tasks where you have tabular data and want to avoid manual model selection. The time series forecasting capability with automatic seasonal period detection via ACF makes it useful for forecasting workflows. The tool is appropriate for initial exploration phases rather than production optimization, since it trains models with default parameters and focuses on breadth over depth.
The project shows active development with regular updates to support new Python versions and emerging model types. The codebase incorporates modern acceleration techniques including GPU support and Intel Extension for Scikit-learn integration. The maintainers have expanded the scope significantly beyond basic classification and regression to include comprehensive time series forecasting with diverse model families. Documentation is maintained separately and the project includes configurable options for cross-validation, timeout limits, custom metrics, and verbosity to adapt to different workflows.
