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ATM model for a DCA experiment
Optimization and inter-model variance: Use a ninth shipping model with the nine new features. Randomly subsample to keep only 10% of the training DBs (although we still use all validation DBs). https://ml.azure.com/runs/classification_1661794934_fc5db59f?wsid=/subscriptions/91095667-e119-4555-acea-1826488492f0/resourcegroups/ATM-CodeML/workspaces/ATM-Workspace&tid=398a6654-997b-47e9-b12b-9515b896b4de
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dsp-testing/javascript-experimental-atm-model:
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javascript/ql/experimental/adaptivethreatmodeling/src/qlpack.yml

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dsp-testing/javascript-experimental-atm-model: 0.0.43+2022-08-22-11h30m37s.amiable-cord-xpz5rmfl.a4a52ee36a4e2ff0b25bf82ad5df97f658a20c12e60606de520405b307c3de67
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dsp-testing/javascript-experimental-atm-model: 0.0.92+2022-08-29-10h42m07s.lime-plane-b8gjvxnl.f84ecc8434cfe2cc0d1bc49ba13cea53490f6ff548d6018b331744da31e55e16

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