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cd43c52cf9
Heating is the dominant SAP lever (ablating it to actual cut the SAP error ~7 -> ~4.5) yet was entirely unscored. Add the heating group to compare_prediction's categorical_hits: main fuel / category / control (off the primary MainHeatingDetail), water-heating fuel / code, has-cylinder, cylinder insulation, secondary heating (off SapHeating). Template-copied baseline on the 40-postcode corpus (no predictor change yet — this just makes the signal visible): heating_main_fuel 93.4% heating_main_category 92.7% water_heating_fuel/code 91.7% / 92.4% heating_main_control 62.1% <- weak has_hot_water_cylinder 78.5% cylinder_insulation_type 35.8% (n=120) <- weak secondary_heating_type 16.8% (n=125) <- weak Fuel/category predict well from the template; controls, cylinder, and secondary heating are poor and now drive the next predictor slices. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .. | ||
| addresses | ||
| billing | ||
| data_transformation | ||
| epc | ||
| epc_prediction | ||
| fuel_rates | ||
| geospatial | ||
| magicplan | ||
| modelling | ||
| property | ||
| property_baseline | ||
| sap10_calculator | ||
| sap10_ml | ||
| tasks | ||
| building_geometry.py | ||
| postcode.py | ||