GPC Encoder (Geometry Preserving Category Encoder)

Introduction & Purpose

In ML/DL preprocessing, categorical data is commonly encoded using one-hot, label, or target mean encoding, each of which has clear limitations:

• One-Hot Encoding

  • Causes a rapid increase in dimensionality for high-cardinality features
  • Increases model complexity and memory usage

• Label Encoding

  • Introduces artificial ordinal relationships between categories
  • Can mislead linear or distance-based models

• Target Mean Encoding

  • Prone to data leakage, as global target statistics influence encoding
  • Unstable for rare categories or small datasets, leading to bias

The purpose of GPC Encoder is to address all of these issues by designing an encoding method that:

1. Does not impose any ordering on categories
2. Does not drastically increase dimensionality
3. Avoids data leakage by relying only on training data statistics

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Requirements

• Python 3.11.4
• See requirements.txt for full dependency details