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.

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House Price Prediction#

Regression task demonstrating numeric feature engineering, scaling, and handling of mixed data types.

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San Francisco Crime Classification#

Multi-class classification with datetime feature engineering, text processing, and geographic features.

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Fraud Detection#

Imbalanced classification with advanced feature generation, group-based statistics, and model evaluation.

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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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