Weight of Evidence and Information Value: A Beginner’s Guide Credit Scoring and Feature Selection

1. 1Read the article on Medium to understand the basics of Weight of Evidence and Information Value
2. 2Apply the Weight of Evidence formula to a sample dataset to calculate the correlation between variables
3. 3Use Information Value to select the most relevant features for a credit scoring model
4. 4Compare the performance of different models using Weight of Evidence and Information Value
5. 5Implement feature selection techniques using Python libraries such as scikit-learn or pandas

Data scientists and machine learning engineers can benefit from this guide to improve their credit scoring models and feature selection techniques

💡 Weight of Evidence and Information Value are essential metrics for evaluating the strength of variables in credit scoring models

Boost your credit scoring models with Weight of Evidence and Information Value!