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# sklearn IsolationForest.score_samples reference scores
# lower = more anomalous (this is sklearn's sign convention)
# dataset: seeds.csv (sha256 9e179a953639a49adedb72cff7a1203b10b02ed5d92255e8c66539ab3d6bb5cf)
# rows: 210, features: 7
# sklearn 1.9.0, python 3.12.13
# n_estimators=100 max_samples=256 random_state=42 contamination=auto
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