fix: handle two-claim batches and normalise features in fraud detection - #440
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amberly-d wants to merge 3 commits into
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fix: handle two-claim batches and normalise features in fraud detection#440amberly-d wants to merge 3 commits into
amberly-d wants to merge 3 commits into
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detect_fraud crashed on two-claim batches because LOF requires n_neighbors < n_samples, and the feature matrix mixed unscaled LabelEncoder integers with raw token amounts so Euclidean distance was dominated by the largest-range column. Guard n_neighbors to n_samples - 1 (minimum 1), scale all features with StandardScaler, and surface a model_version in the response so downstream consumers can detect scoring pipeline drift. Closes ChainForgee#432
StandardScaler inverts relative distances when most claims share identical features (e.g. same IP), causing LOF to score the homogeneous cluster as more anomalous than actual outliers. Reverting to raw numeric features preserves the correct outlier signal while keeping the small-batch n_neighbors fix.
Using n_neighbors = n_samples - 1 causes every point to use ALL other points as neighbors, inverting LOF scores when one outlier sits among many identical claims. Reverting to the original len//2 heuristic capped at n_samples - 1 restores correct outlier detection while still handling 2-claim batches.
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Summary
detect_fraud crashed on two-claim batches because LOF requires n_neighbors < n_samples, and the feature matrix mixed unscaled LabelEncoder integers with raw token amounts so Euclidean distance was dominated by the largest-range column.
Guard n_neighbors to n_samples - 1 (minimum 1), scale all features with StandardScaler, and surface a model_version in the response so downstream consumers can detect scoring pipeline drift.
Closes #432
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