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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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| domain | ||
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| magic_plan | ||
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| utilities | ||
| __init__.py | ||
| conftest.py | ||
| test_lambda_packaging.py | ||