Automatic Model Selection
Provides LazyClassifier and LazyRegressor classes to automatically build and compare multiple classification and regression models without parameter tuning.
Support for Numerical and Categorical Features
Handles both numerical and categorical data, including splitting categorical columns into low and high cardinality pipelines for better processing.
Integration with scikit-learn Pipelines
Easily integrates with scikit-learn pipelines to fit into existing machine learning workflows.
Model Performance Comparison
Generates tables comparing models on metrics such as accuracy, balanced accuracy, ROC AUC, and training time to facilitate model selection.
MLflow Integration
Supports MLflow for experiment tracking to manage and record model training runs.
Custom Evaluation Metrics and Predictions
Allows users to input custom evaluation metrics and returns predictions as DataFrames for further analysis.