📊 Understanding Weight of Evidence (WoE) & Information Value (IV) for Predictive Modeling
🔍 With Rajat | Data Science Simplified

In this video, Rajav breaks down two fundamental concepts in predictive modeling: Weight of Evidence (WoE) and Information Value (IV). These techniques are crucial for variable selection and are often featured in data science and analytics interviews.

📌 Timestamps & Highlights:
00:00 – Introduction to WoE & IV
00:27 – What is Weight of Evidence?
00:53 – Recap: WoE and its role in prediction
01:52 – Using the “default flag” as a dependent variable
02:31 – Binning salary ranges for WoE analysis
03:40 – Comparing good vs. bad customers by salary
04:22 – How to calculate WoE
04:56 – Interpreting example WoE values
05:38 – Transition to Information Value (IV)
06:12 – IV examples with salary & education
07:22 – Why WoE & IV matter in modeling and interviews
08:32 – Variable reduction using IV
09:05 – How to interpret IV scores
09:58 – IV score thresholds (0 to 1 scale)
12:21 – Calculating IV step-by-step

🧠 Key Takeaways:
WoE measures how well an independent variable predicts a target variable

IV quantifies the overall predictive strength of each feature

Learn how to bin variables, calculate WoE & IV, and use them for feature selection in modeling

Essential concepts for credit scoring, risk modeling, and ML preprocessing

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