At a glance
Task: classification | Headline: accuracy = 1.0
Summary
Project
Config-driven data science pipeline for high-fidelity industrial domains.
What was investigated
The engagement modelled the target 'is_compliant' as a classification problem in the ISM + Security: AI Governance & Data Provenance domain, using 2000 records across 23 columns (data source: Synthetic sandbox). The pipeline audited data quality, engineered features, split the data honestly into train/test, and compared several models by cross-validation.
Outcome
The selected model was LogisticRegression, with f1_macro = 1.0000 on the held-out test set. Supporting metrics: accuracy=1.000, balanced_accuracy=1.000, precision_weighted=1.000, recall_weighted=1.000, f1_weighted=1.000, precision_macro=1.000, recall_macro=1.000, cohen_kappa=1.000, matthews_. Full results, the deployment gates and the audit trail are in the run's report.
Figures
Artifacts
Data source: Synthetic sandbox
Download report (.docx)Audit SHA-256: 678d40bca5df6feefb4dbb6cc4b35ef30d887b66c619f6cf9606a8265bd6f534