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

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1.
Discover how to use target encoding and weight of evidence for transforming categorical variables in supervised learning, enhancing model performance.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ml-credit-risk-models, #target-encoding, #ml-models, #output-encoding, #logistic-regression, #piecewise-constant-model, #predictive-ml-modelling, #ml-model-optimization, and more.

This story was written by: @varunnakra1. Learn more about this writer by checking @varunnakra1's about page, and for more stories, please visit hackernoon.com.

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.