shankarpandala/lazypredict
AutoML library that trains and ranks 40+ ML models for classification, regression, and time series forecasting without parameter tuning.
LazyPredict is an automated machine learning library that trains multiple baseline models with default hyperparameters and ranks their performance to help users quickly identify which algorithms work best for their data. It supports classification, regression, and time series forecasting tasks, with built-in models ranging from classical algorithms like Random Forest and XGBoost to deep learning models like LSTM and GRU, as well as foundation models like TimesFM. The library includes GPU acceleration support, MLflow integration for experiment tracking, and configurable cross-validation and timeout settings.
Frequently asked
- What is shankarpandala/lazypredict?
- AutoML library that trains and ranks 40+ ML models for classification, regression, and time series forecasting without parameter tuning.
- Is lazypredict open source?
- Yes — shankarpandala/lazypredict is open source, released under the MIT license.
- What language is lazypredict written in?
- shankarpandala/lazypredict is primarily written in Python.
- How popular is lazypredict?
- shankarpandala/lazypredict has 3.3k stars on GitHub.
- Where can I find lazypredict?
- shankarpandala/lazypredict is on GitHub at https://github.com/shankarpandala/lazypredict.