cpa-analytics/embedding-encoder
Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
When building predictive models, you often work with categories like product types or geographic regions. This tool helps transform these categorical details into a numeric format that machine learning models can understand better, potentially improving their accuracy. It takes your dataset with categorical columns and a target variable, then outputs a modified dataset where those categories are represented by dense numerical vectors. This is designed for data scientists or machine learning engineers who are preparing data for predictive modeling tasks.
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Use this if you need to convert categorical features into rich, numerical representations for machine learning models within a scikit-learn workflow.
Not ideal if your primary goal is simple one-hot encoding or if you prefer not to use neural network-based feature engineering.
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42
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Language
Jupyter Notebook
License
MIT
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Last pushed
Aug 14, 2023
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