Examples#
This section contains end-to-end examples demonstrating how to use Gators for real-world machine learning tasks.
Overview#
Each example notebook demonstrates a complete ML workflow using Gators transformers:
Data loading and exploration
Feature engineering pipeline construction
Model training and evaluation
Performance comparison with traditional methods
Titanic Survival Prediction#
Binary classification using advanced feature engineering including string parsing, mathematical features, and rare category encoding.
House Price Prediction#
Regression task demonstrating numeric feature engineering, scaling, and handling of mixed data types.
San Francisco Crime Classification#
Multi-class classification with datetime feature engineering, text processing, and geographic features.
Fraud Detection#
Imbalanced classification with advanced feature generation, group-based statistics, and model evaluation.
Performance Benchmarks#
Benchmark Gators vs Sklearn Encoders#
Performance comparison of Gators encoders against sklearn encoders across different dataset sizes, demonstrating Polars’ multi-core processing advantages.
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Benchmark Gators vs Feature-Engine Encoders#
Performance comparison of Gators encoders against feature-engine encoders, showcasing speedup across OneHot, Ordinal, Count, and RareCategory encoding transformers.
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Benchmark Gators vs Sklearn Imputers#
Performance comparison of Gators imputers against sklearn imputers across different dataset sizes, comparing NumericImputer, StringImputer, and BooleanImputer with multiple strategies.
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Benchmark Gators vs Feature-Engine Imputers#
Performance comparison of Gators imputers against feature-engine imputers, showcasing speedup across mean, median, constant, and categorical imputation strategies.
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Benchmark Gators vs Sklearn Scalers#
Performance comparison of Gators scalers against sklearn scalers across different dataset sizes, comparing StandardScaler, MinMaxScaler, BoxCox, and YeoJohnson transformations.
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Benchmark Gators vs Feature-Engine Scalers/Transformers#
Performance comparison of Gators scalers against feature-engine transformers and sklearn scalers, showcasing speedup across BoxCox, YeoJohnson power transformations and traditional scalers.
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Benchmark Gators vs Feature-Engine Datetime Features#
Performance comparison of Gators datetime feature generators against feature-engine DatetimeFeatures, showcasing speedup across ordinal features (year, month, day, etc.), day of week, and quarter extraction.
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