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摘要: Bayesian Target Encoding is a feature engineering technique used to map categorical variables into numeric variables. The Bayesian framework requires only minimal updates as new data is acquired and is thus well-suited for online learning. Furthermore, the Bayesian approach makes choosing and interpreting hyperparameters intuitive. I developed this technique in the recent Avito Kaggle Competition, where my team and I took 14th place out of 1,917 teams. We found that the Bayesian target encoding outperforms the built-in categorical encoding provided by the LightGBM package.

 


Target-encoded variables are inherently leaky; that is, their construction requires information that we will not have when we make predictions.

By adopting a Bayesian framework, we can handle regularization in a systematic yet intuitive way. Furthermore, the Bayesian framework is well-suited for online learning requiring only minimal updates as new data is acquired. In the next section, we will discuss target encoding in the context of binary classification.

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Full Text: mattmotoki.github

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