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275a30a825
Finish the ADR-0030 Component Accuracy set: roof insulation thickness, floor insulation, room-in-roof presence, modal glazing type, PV presence, solar water heating (categoricals) + door count (residual). Presence flags (room-in-roof, PV, solar) are always-applicable — predicting absence when present is a real miss. Template-copied baseline (40-postcode corpus), newly visible: floor_insulation 94.0% solar_water_heating 99.7% has_pv 98.6% has_room_in_roof 91.9% modal_glazing_type 59.0% <- weak roof_insulation_thickness 30.6% <- weak door_count mean|.| 0.40 compare_prediction now scores 19 categoricals + 5 residuals across every SAP-load-bearing component group. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .. | ||
| eon | ||
| analyse_api_sap_clusters.py | ||
| decompose_api_cost_error.py | ||
| download_cotality_evidence.py | ||
| elmhurst_input_sheet.py | ||
| eval_api_sap_accuracy.py | ||
| fetch_2026_epc_sample.py | ||
| fetch_cohort2_api_jsons.py | ||
| fetch_epc_bulk_sample.py | ||
| fetch_epc_dump.py | ||
| fetch_epc_prediction_corpus.py | ||
| historic_epc_demo.py | ||
| init_db.py | ||
| profile_api_error.py | ||
| rename_sharepoint_files.py | ||
| run_audit_generator_local.py | ||
| run_modelling_cohort.py | ||
| run_modelling_e2e.py | ||
| run_property_report.py | ||
| sero_address_list.csv | ||
| validate_epc_prediction.py | ||