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conda-forge.org : category_encoders
A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques. While ordinal, one-hot, and hashing encoders have similar equivalents in the existing scikit-learn version, the transformers in this library all share a few useful properties: - First-class support for pandas dataframes as an input (and optionally as output) - Can explicitly configure which columns in the data are encoded by name or index, or infer non-numeric columns regardless of input type - Can drop any columns with very low variance based on training set optionally - Portability: train a transformer on data, pickle it, reuse it later and get the same thing out. - Full compatibility with sklearn pipelines, input an array-like dataset like any other transformer
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purl: pkg:conda/category_encoders
License: BSD-3-Clause
Latest release: almost 2 years ago
First release: over 1 year ago
Dependent packages: 7
Dependent repositories: 12
Stars: 2,180 on GitHub
Forks: 383 on GitHub
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Last synced: 7 days ago