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A collection sklearn transformers to encode categorical variables as numeric

Project description

A set of example problems examining different encoding methods for categorical variables for the purpose of classification. Optionally, install the library of encoders as a package and use them in your projects directly. They are all available as methods or as scikit-learn compatible transformers.

Docs [here](http://wdm0006.github.io/categorical_encoding/)

Encoding Methods

  • Ordinal
  • One-Hot
  • Binary
  • Helmert Contrast
  • Sum Contrast
  • Polynomial Contrast
  • Backward Difference Contrast
  • Simple Hashing

Usage

Either run the examples in encoding_examples.py, or install as:

pip install category_encoders

To use:

import category_encoders as ce

encoder = ce.BackwardDifferenceEncoder(cols=[…]) encoder = ce.BinaryEncoder(cols=[…]) encoder = ce.HashingEncoder(cols=[…]) encoder = ce.HelmertEncoder(cols=[…]) encoder = ce.OneHotEncoder(cols=[…]) encoder = ce.OrdinalEncoder(cols=[…]) encoder = ce.SumEncoder(cols=[…]) encoder = ce.PolynomialEncoder(cols=[…])

All of these are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. If the cols parameter isn’t passed, every column will be encoded, so be careful with that.

Datasets

The datasets used in the examples are car, mushroom, and splice datasets from the UCI dataset repository, found here:

[datasets](https://archive.ics.uci.edu/ml/datasets)

License

BSD

Project details


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