Interpretability summary: house_prices_8arch_interp
Performance ranking
| Rank | Model | Test Gini | Test MAE | A/E ratio | n params | Train time |
|---|---|---|---|---|---|---|
| 1 | xgboost | 0.2049 | 17203.89 | 0.999 | 462 | 0.4s |
| 2 | stacked_ensemble | 0.2049 | 17203.89 | 0.999 | 9 | 0.0s |
| 3 | catboost | 0.1996 | 29223.45 | 1.161 | 499 | 2.7s |
| 4 | localglmnet | 0.1991 | 23419.59 | 0.988 | 22,620 | 6.3s |
| 5 | drn | 0.1962 | 27927.62 | 0.981 | 53,010 | 6.6s |
| 6 | cann | 0.1941 | 24906.06 | 1.024 | 52,815 | 6.5s |
| 7 | cann_gbm | 0.1940 | 32932.26 | 1.193 | 52,815 | 5.8s |
| 8 | ft_transformer | 0.0368 | 187771.09 | 3337.260 | 483,267 | 340.8s |
| 9 | tabm | 0.0331 | 187802.11 | 7436.716 | 410,364 | 176.6s |
Top-10 features per architecture
| Rank | catboost | xgboost |
|---|---|---|
| 1 | TotalSF (0.096) |
TotalSF (0.138) |
| 2 | OverallQual (0.043) |
OverallQual (0.103) |
| 3 | GrLivArea (0.043) |
GrLivArea (0.032) |
| 4 | YearRemodAdd (0.038) |
YearRemodAdd (0.025) |
| 5 | LotArea (0.030) |
OverallCond (0.025) |
| 6 | KitchenQual (0.028) |
HouseAge (0.021) |
| 7 | OverallCond (0.028) |
GarageCars (0.020) |
| 8 | MSZoning (0.022) |
LotArea (0.019) |
| 9 | HouseAge (0.022) |
YearBuilt (0.012) |
| 10 | GarageCars (0.021) |
KitchenQual (0.011) |
Cross-method agreement
Features that appear in top-5 across every model with importance scores:
GrLivAreaOverallQualTotalSFYearRemodAdd
This cross-method agreement is a strong signal - when both tree-based SHAP and gradient-based Captum IG identify the same feature as critical, the finding is unlikely to be a method-specific artefact.
LocalGLMnet coefficient analysis
LocalGLMnet emits one row of coefficients per test record. We summarise the distribution of each feature's coefficient across the sampled test set (mean ± std):
| Feature | Mean coef | Std | Sign stability |
|---|---|---|---|
LotArea |
2.912e-04 | 1.847e-04 | 98% |
YearBuilt |
3.377e-04 | 2.296e-04 | 99% |
YearRemodAdd |
3.083e-04 | 2.715e-04 | 97% |
TotalBsmtSF |
2.400e-04 | 1.464e-04 | 99% |
1stFlrSF |
2.361e-04 | 1.171e-04 | 100% |
2ndFlrSF |
-2.467e-04 | 2.454e-04 | 96% |
GrLivArea |
-5.616e-05 | 1.236e-04 | 72% |
FullBath |
1.693e-04 | 2.968e-04 | 86% |
BedroomAbvGr |
-9.640e-05 | 1.435e-04 | 80% |
TotRmsAbvGrd |
-1.148e-04 | 1.200e-04 | 84% |
GarageCars |
3.736e-04 | 4.435e-04 | 98% |
GarageArea |
2.725e-04 | 1.512e-04 | 99% |
OverallQual |
1.848e-04 | 1.240e-04 | 98% |
OverallCond |
2.792e-04 | 3.282e-04 | 99% |
TotalSF |
2.274e-04 | 1.413e-04 | 99% |
HouseAge |
-3.340e-04 | 2.015e-04 | 100% |
LotArea_raw |
3.759e-08 | 2.385e-08 | 98% |
YearBuilt_raw |
1.106e-05 | 7.519e-06 | 99% |
YearRemodAdd_raw |
1.493e-05 | 1.314e-05 | 97% |
TotalBsmtSF_raw |
5.483e-07 | 3.345e-07 | 99% |
1stFlrSF_raw |
6.213e-07 | 3.082e-07 | 100% |
2ndFlrSF_raw |
-5.686e-07 | 5.655e-07 | 96% |
GrLivArea_raw |
-1.075e-07 | 2.365e-07 | 72% |
FullBath_raw |
3.079e-04 | 5.398e-04 | 86% |
BedroomAbvGr_raw |
-1.181e-04 | 1.757e-04 | 80% |
TotRmsAbvGrd_raw |
-7.042e-05 | 7.359e-05 | 84% |
GarageCars_raw |
5.017e-04 | 5.955e-04 | 98% |
GarageArea_raw |
1.298e-06 | 7.199e-07 | 99% |
OverallQual_raw |
1.338e-04 | 8.976e-05 | 98% |
OverallCond_raw |
2.500e-04 | 2.938e-04 | 99% |
TotalSF_raw |
2.768e-07 | 1.720e-07 | 99% |
HouseAge_raw |
-1.091e-05 | 6.583e-06 | 100% |
Sign stability is the fraction of test records where the coefficient has the same sign as the mean. Values close to 100% mean the model is confident about that feature's direction; values closer to 50% mean the feature's effect flips across records (which is exactly what LocalGLMnet was designed to detect).
Artefacts on disk
dashboard_dl_interpretability.html- interactive Plotly dashboard with SHAP beeswarm plots, Captum IG heatmaps, FT-Transformer attention matrices, CANN residual histograms.localglmnet_coefficients.csv- per-test-record coefficients from LocalGLMnet (one row per test record, one column per continuous feature).feature_importance.csv- consolidated importances (CatBoost / XGBoost native importance scores).drn_distributional_outputs.csv- DRN's predictive distribution moments (mean, variance, quantiles) per test row.