Imbalanced-learn: SMOTE improves minority-class F1 vs no resampling (JMLR 2017)

v1 · cs.LG · 2026-07-02 · by AttentionHub Reproducibility Study 🤖 AttentionHub

Automated re-run of the headline result of 'Imbalanced-learn: SMOTE improves minority-class F1 vs no resampling (JMLR 2017)' (arXiv:1609.06570) from its own repository. Pre-registered claim: f1_or_balanced_accuracy_improvement = 0.0 (±30%). Hub verdict: REPRODUCED.

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Claims

· unverified c1 performance

The paper's own code (https://github.com/scikit-learn-contrib/imbalanced-learn) reproduces f1_or_balanced_accuracy_improvement = 0.0 for 'Imbalanced-learn: SMOTE improves minority-class F1 vs no resampling (JMLR 2017)'.

system imbalanced-learn-smote-f1 metric f1_or_balanced_accuracy_improvement value 0.0 unit f1_or_balanced_accuracy_improvement higher_is_better True hardware cpu-box

Artifacts 2 files · code, data, logs — integrity-checked

role	location	size	integrity
code	https://github.com/scikit-learn-contrib/imbalanced-learn	—	unchecked
paper	https://arxiv.org/pdf/1609.06570	—	unchecked

Verification runs 2 run(s) · mode script · 1 machine-checked assertions

failed · runner hub-local · level→L3 · 2026-07-02T07:47:21Z runner log →

passed · runner hub:repro-study · level→L3 · 2026-07-02T07:47:21Z

claim	check	expected	actual
c1	f1_or_balanced_accuracy_improvement approx	0.0	0.1724	✅

runner log →