KS Statistic

 The KS (Kolmogorov-Smirnov) Statistic is a powerful and commonly used evaluation metric for binary classification models, especially in finance, credit scoring, and risk modeling.

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📊 What is KS Statistic?

The KS statistic measures the maximum difference between the cumulative distribution functions (CDFs) of the predicted scores for the positive class (events) and negative class (non-events).

Formula:

$$KS = \max_x |F_1(x) - F_0(x)|$$

Where:

• $F_1(x)$: Cumulative distribution of positive class (e.g., default)

• $F_0(x)$: Cumulative distribution of negative class (e.g., non-default)

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🧠 Intuition:

• It tells how well the model separates the two classes.

• A higher KS value means better separation of good and bad cases.

• KS = 0: no separation (useless model)

• KS = 1: perfect separation (ideal but unrealistic)

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📌 Usage by Domain

Domain	Why KS is Used
Banking / Credit Risk	Industry standard for measuring discriminatory power between defaulters and non-defaulters
Insurance	Distinguishing claimants vs non-claimants
Fraud Detection	Separating fraudulent from legitimate transactions
Marketing	Used less commonly; better suited metrics include precision@k and lift

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✅ Typical KS Value Interpretation:

KS Score	Model Quality
< 0.2	Poor
0.2 - 0.3	Fair
0.3 - 0.4	Good
> 0.4	Excellent