KNIME side-by-side equivalent of m9x_train.py: chronological split, past-only feature engineering, Random Forest (class-weight balanced analog), precision/recall + cost-aware threshold for imbalanced pump anomaly detection.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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