Lazypredict
Lazypredict is an open-source Python library designed to quickly build and compare multiple basic machine learning models for classification and regression tasks with minimal coding effort. It automates the process of training various models without parameter tuning, providing performance metrics such as accuracy, balanced accuracy, ROC AUC, and training time to help users identify which models perform better on their datasets. The library supports both numerical and categorical features, including handling categorical columns based on cardinality, and integrates seamlessly with scikit-learn pipelines. The tool also offers MLflow integration for experiment tracking and allows users to input custom evaluation metrics. Lazypredict is distributed under the MIT license and can be installed easily via pip. Its last major update was in 2021, and it currently supports scikit-learn compatible models, excluding some previously included models like CatBoost. It is targeted at data scientists and machine learning practitioners who want to quickly generate baseline model comparisons without extensive coding or hyperparameter tuning.
Lazypredict is a Python library that automates building and comparing multiple basic machine learning models for classification and regression with minimal code.
Baseline Model Comparison
Data scientists need to quickly evaluate multiple machine learning models on a new dataset to identify promising candidates without extensive coding or tuning.
pip install lazypredict to install the library.lazypredict.Supervised depending on your task.train_test_split.fit method with training and testing data to get model performance comparisons.