Information Value (IV) vs SHAP Values: Key Differences

 

Both Information Value (IV) and SHAP values help in understanding the importance of features in a model, but they have different applications and interpretations.

Feature	Information Value (IV)	SHAP Values (SHapley Additive Explanations)
Purpose	Measures the predictive power of a feature in a classification model.	Explains how each feature contributes to an individual model prediction.
Type of Importance	Global: Ranks features based on their overall impact on predictions.	Local + Global: Provides importance per prediction and overall feature ranking.
Interpretation	Higher IV means a feature separates target classes well.	Positive/negative SHAP values show how much a feature pushes the prediction up or down.
Works With	Logistic Regression, Credit Scoring Models.	Any ML model (Tree-based models, Deep Learning, etc.).
Mathematical Basis	Weight of Evidence (WOE): Measures how well a feature separates the target classes.	Game Theory (Shapley Values): Measures each feature’s contribution to the prediction.
Use Case	Feature selection for classification problems (e.g., credit risk models).	Model explainability for black-box models (e.g., random forests, XGBoost, neural networks).

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1️⃣ What is Information Value (IV)?

Information Value (IV) is used to measure how predictive a feature is in separating two classes (e.g., fraud vs. non-fraud, churn vs. non-churn). It is derived from Weight of Evidence (WOE).

Formula for IV

$$IV = \sum \left( \text{WOE} \times (\% \text{ Good } - \% \text{ Bad }) \right)$$

Where:

• WOE (Weight of Evidence) = $\ln (\frac{\% \text{ Good }}{\% \text{ Bad }})$
• Good and Bad refer to class distributions (e.g., non-churn vs. churn)

How to Interpret IV

IV Value	Predictive Power
< 0.02	Not useful
0.02 - 0.1	Weak predictor
0.1 - 0.3	Medium predictor
0.3 - 0.5	Strong predictor
> 0.5	Very strong predictor

Example of IV Calculation

Consider a credit risk model where we analyze the feature "Credit Score" for predicting default (Yes/No).

Credit Score Bin	% Good (No Default)	% Bad (Default)	WOE	IV Contribution
300-500	10%	50%	-1.61	0.64
500-700	40%	40%	0.00	0.00
700-850	50%	10%	1.61	0.64

Total IV = 1.28, meaning "Credit Score" is a very strong predictor.

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2️⃣ What is SHAP (Shapley Values)?

SHAP values explain how much each feature contributes to the model’s prediction for a given instance.

Key Idea

• The SHAP value of a feature tells how much it increases or decreases the model’s prediction compared to the average.
• It is based on game theory, treating each feature as a "player" contributing to the outcome.

How to Interpret SHAP

• Positive SHAP: Increases the predicted value.
• Negative SHAP: Decreases the predicted value.
• Magnitude: The larger the SHAP value, the more significant the feature’s contribution.

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Example: Comparing IV vs. SHAP in a Credit Model

Imagine we are predicting loan default (Yes/No) using Age, Credit Score, and Income.

1️⃣ Information Value (IV)

Feature	IV Value	Importance
Credit Score	0.75	Very strong
Income	0.40	Strong
Age	0.20	Medium

Interpretation:

• Credit Score is the most important predictor at a global level.
• IV does not show how these features affect individual predictions.

2️⃣ SHAP Values for a Specific Prediction

Example: Predicting Default Probability for a Person

• Person A: Age = 45, Credit Score = 600, Income = $50,000
• Model Output: Predicted Probability of Default = 0.65 (65%)

Feature	SHAP Value	Contribution to Prediction
Credit Score	+0.20	Increases default risk
Income	-0.15	Decreases default risk
Age	+0.10	Increases default risk

Interpretation:

• Credit Score (600) increased default risk by 20%.
• Income decreased risk by 15%.
• Age increased risk by 10%.
• The final probability is 0.65 based on these contributions.

🔹 SHAP gives local explainability for this specific person’s prediction, while IV only provides global feature importance.

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🚀 Key Takeaways

Feature	Information Value (IV)	SHAP Values
Measures	Overall feature importance	Individual prediction contribution
Scope	Global (across dataset)	Local + Global
Mathematics	Weight of Evidence (WOE)	Shapley Values (Game Theory)
Use Cases	Feature selection, credit scoring	Explaining model decisions, fairness auditing
Models Supported	Logistic Regression, Scorecards	Any ML model (XGBoost, Deep Learning, etc.)

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🎯 When to Use Which?

✅ Use IV when:

• You are selecting features for a classification model.
• You need to evaluate predictive power globally.

✅ Use SHAP when:

• You need model interpretability (why a model made a specific decision).
• You are working with complex models like XGBoost, Random Forests, Deep Learning.
• You need both local and global importance explanations.