How To Use Target Encoding in Machine Learning Credit Risk Models – Part 1

Machine Learning Tech Brief By HackerNoon

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Discover how to use target encoding and weight of evidence for transforming categorical variables in supervised learning, enhancing model performance.
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Target encoding transforms categorical variables into numerical values based on the target variable, while Weight of Evidence (WoE) applies this concept to continuous variables for binary classification. WoE calculates log-odds differences between specific regions and overall averages, offering a powerful tool for credit risk modeling and other applications.